Athero-Express Biobank Study – IL6 and MCP1 plaque levels.
An ‘Athero-Express Biobank Study’ project
Sander W. van der Laan, PhD | @swvanderlaan; Marios Georgakis; Rainer Malik; Martin Dichgans
2020-07-06
Preparation
Clean the environment.
Set locations, and the working directory.
A package-installation function.
Load those packages.
We will create a datestamp and define the Utrecht Science Park Colour Scheme.
# Function to grep data from glm()/lm()
GLM.CON <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' .\n")
if (nrow(summary(fit)$coefficients) == 1) {
output = c(DATASET, x_name, y, rep(NA,8))
cat("Model not fitted; probably singular.\n")
}else {
cat("Collecting data.\n\n")
effectsize = summary(fit)$coefficients[2,1]
SE = summary(fit)$coefficients[2,2]
OReffect = exp(summary(fit)$coefficients[2,1])
CI_low = exp(effectsize - 1.96 * SE)
CI_up = exp(effectsize + 1.96 * SE)
tvalue = summary(fit)$coefficients[2,3]
pvalue = summary(fit)$coefficients[2,4]
R = summary(fit)$r.squared
R.adj = summary(fit)$adj.r.squared
sample_size = nrow(model.frame(fit))
AE_N = AEDB.CEA.samplesize
Perc_Miss = 100 - ((sample_size * 100)/AE_N)
output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, tvalue, pvalue, R, R.adj, AE_N, sample_size, Perc_Miss)
if (verbose == TRUE) {
cat("We have collected the following and summarize it in an object:\n")
cat("Dataset...................:", DATASET, "\n")
cat("Score/Exposure/biomarker..:", x_name, "\n")
cat("Trait/outcome.............:", y, "\n")
cat("Effect size...............:", round(effectsize, 6), "\n")
cat("Standard error............:", round(SE, 6), "\n")
cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
cat("T-value...................:", round(tvalue, 6), "\n")
cat("P-value...................:", signif(pvalue, 8), "\n")
cat("R^2.......................:", round(R, 6), "\n")
cat("Adjusted r^2..............:", round(R.adj, 6), "\n")
cat("Sample size of AE DB......:", AE_N, "\n")
cat("Sample size of model......:", sample_size, "\n")
cat("Missing data %............:", round(Perc_Miss, 6), "\n")
} else {
cat("Collecting data in summary object.\n")
}
}
return(output)
print(output)
}
GLM.BIN <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' ...\n")
if (nrow(summary(fit)$coefficients) == 1) {
output = c(DATASET, x_name, y, rep(NA,9))
cat("Model not fitted; probably singular.\n")
}else {
cat("Collecting data...\n")
effectsize = summary(fit)$coefficients[2,1]
SE = summary(fit)$coefficients[2,2]
OReffect = exp(summary(fit)$coefficients[2,1])
CI_low = exp(effectsize - 1.96 * SE)
CI_up = exp(effectsize + 1.96 * SE)
zvalue = summary(fit)$coefficients[2,3]
pvalue = summary(fit)$coefficients[2,4]
dev <- fit$deviance
nullDev <- fit$null.deviance
modelN <- length(fit$fitted.values)
R.l <- 1 - dev / nullDev
R.cs <- 1 - exp(-(nullDev - dev) / modelN)
R.n <- R.cs / (1 - (exp(-nullDev/modelN)))
sample_size = nrow(model.frame(fit))
AE_N = AEDB.CEA.samplesize
Perc_Miss = 100 - ((sample_size * 100)/AE_N)
output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, zvalue, pvalue, R.l, R.cs, R.n, AE_N, sample_size, Perc_Miss)
if (verbose == TRUE) {
cat("We have collected the following and summarize it in an object:\n")
cat("Dataset...................:", DATASET, "\n")
cat("Score/Exposure/biomarker..:", x_name, "\n")
cat("Trait/outcome.............:", y, "\n")
cat("Effect size...............:", round(effectsize, 6), "\n")
cat("Standard error............:", round(SE, 6), "\n")
cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
cat("Z-value...................:", round(zvalue, 6), "\n")
cat("P-value...................:", signif(pvalue, 8), "\n")
cat("Hosmer and Lemeshow r^2...:", round(R.l, 6), "\n")
cat("Cox and Snell r^2.........:", round(R.cs, 6), "\n")
cat("Nagelkerke's pseudo r^2...:", round(R.n, 6), "\n")
cat("Sample size of AE DB......:", AE_N, "\n")
cat("Sample size of model......:", sample_size, "\n")
cat("Missing data %............:", round(Perc_Miss, 6), "\n")
} else {
cat("Collecting data in summary object.\n")
}
}
return(output)
print(output)
}Background
Using a Mendelian Randomization approach, we recently examined associations between the circulating levels of 41 cytokines and growth factors and the risk of stroke in the MEGASTROKE GWAS dataset (67,000 stroke cases and 450,000 controls) and found Monocyte chemoattractant protein-1 (MCP-1) as the cytokine showing the strongest association with stroke, particularly large artery and cardioembolic stroke (Georgakis et al., 2019a). Genetically elevated MCP-1 levels were also associated with a higher risk of coronary artery disease and myocardial infarction (Georgakis et al., 2019a). Further, in a meta-analysis of 6 observational population-based of longitudinal cohort studies we recently showed that baseline levels of MCP-1 were associated with a higher risk of ischemic stroke over follow-up (Georgakis et al., 2019b). While these data suggest a central role of MCP-1 in the pathogenesis of atherosclerosis, it remains unknown if MCP-1 levels in the blood really reflect MCP-1 activity. MCP-1 is expressed in the atherosclerotic plaque and attracts monocytes in the subendothelial space (Nelken et al., 1991; Papadopoulou et al., 2008; Takeya et al., 1993; Wilcox et al., 1994). Thus, MCP-1 levels in the plaque might more strongly reflect MCP-1 signaling. However, it remains unknown if MCP-1 plaque levels associate with plaque vulnerability or risk of cardiovascular events.
Objectives
Against this background we now aim to make use of the data from Athero-Express Biobank Study to explore the associations of MCP-1 protein levels in the atherosclerotic plaques from patients undergoing carotid endarterectomy with phenotypes of plaque vulnerability and secondary vascular events over a follow-up of three years.
Methods
Blood
OLINK-platform
- IL6: Interleukin 6. Entrez Gene: 3569. OLINK, plasma
- MCP1: Monocyte chemotactic protein 1, MCP-1 (Chemokine (C-C motif) ligand 2, CCL2). Entrez Gene: 6347. OLINK, plasma
THESE DATA ARE NOT AVAILABLE YET
Plaque
Luminex-platform, measured by Luminex
- MCP1: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/ug]. Measured in two experiments, variables
MCP1andMCP1_pg_ug_2015. The latter was corrected for plaque total protein concentration. - IL6: Interleuking 6 (IL6; Entrez Gene: 3569) concentration in plaque [pg/ug].
- IL6R: Interleuking 6 receptor (IL6R; Entrez Gene: 3570) concentration in plaque [pg/ug].
FACS platform
- IL6: Interleukin 6. Entrez Gene: 3569. Bender MedSystems; cat.nr.: BMS810FF. Recalculated FACS. [pg/mL]
Loading data
Clinical data
Loading Athero-Express clinical data.
require(haven)
# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))
AEDBraw <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))
head(AEDBraw)Loading Athero-Express plaque protein measurements from 2015.
library(openxlsx)
AEDB_Protein_2015 <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_Proteins/Cytokines_and_chemokines_2015/20200629_MPCF015-0024.xlsx"), sheet = "for_SPSS_R")
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "SampleID"] <- "STUDY_NUMBER"
head(AEDB_Protein_2015)NAWe will merge these measurements to the AEDB for comparing pg/ug vs. pg/mL measurements of MCP1 - also in relation to plaque phenotypes. In addition we have more information the experiment and can correct for this.
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL6_pg_ml"] <- "IL6_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL6R_pg_ml"] <- "IL6R_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL8_pg_ml"] <- "IL8_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "MCP1_pg_ml"] <- "MCP1_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "RANTES_pg_ml"] <- "RANTES_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "PAI1_pg_ml"] <- "PAI1_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "MCSF_pg_ml"] <- "MCSF_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Adiponectin_ng_ml"] <- "Adiponectin_ng_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Segment_isolated_Tris"] <- "Segment_isolated_Tris_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Tris_protein_conc_ug_ml"] <- "Tris_protein_conc_ug_ml_2015"
temp <- subset(AEDB_Protein_2015, select = c("STUDY_NUMBER", "IL6_pg_ml_2015", "IL6R_pg_ml_2015", "IL8_pg_ml_2015", "MCP1_pg_ml_2015", "RANTES_pg_ml_2015", "PAI1_pg_ml_2015", "MCSF_pg_ml_2015", "Adiponectin_ng_ml_2015", "Segment_isolated_Tris_2015", "Tris_protein_conc_ug_ml_2015"))
AEDB <- merge(AEDBraw, temp, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = FALSE,
all.x = TRUE)
rm(temp)
temp <- subset(AEDB, select = c("STUDY_NUMBER", "MCP1", "MCP1_pg_ug_2015", "MCP1_pg_ml_2015", "Segment_isolated_Tris_2015"))
head(temp)NAExamine AEDB
We can examine the contents of the Athero-Express Biobank dataset to know what each variable is called, what class (type) it has, and what the variable description is.
There is an excellent post on this: https://www.r-bloggers.com/working-with-spss-labels-in-r/.
AEDB %>% sjPlot::view_df(show.type = TRUE,
show.frq = TRUE,
show.prc = TRUE,
show.na = TRUE,
max.len = TRUE,
wrap.labels = 20,
verbose = FALSE,
use.viewer = FALSE,
file = paste0(OUT_loc, "/", Today, ".AEDB.dictionary.html")) Fixing and creating variables
We need to be very strict in defining symptoms. Therefore we will fix a new variable that groups symptoms at inclusion.
Coding of symptoms is as follows:
- missing -999
- Asymptomatic 0
- TIA 1
- minor stroke 2
- Major stroke 3
- Amaurosis fugax 4
- Four vessel disease 5
- Vertebrobasilary TIA 7
- Retinal infarction 8
- Symptomatic, but aspecific symtoms 9
- Contralateral symptomatic occlusion 10
- retinal infarction 11
- armclaudication due to occlusion subclavian artery, CEA needed for bypass 12
- retinal infarction + TIAs 13
- Ocular ischemic syndrome 14
- ischemisch glaucoom 15
- subclavian steal syndrome 16
- TGA 17
We will group as follows in Symptoms.5G:
- Asymptomatic > 0
- TIA > 1, 7, 13
- Stroke > 2, 3
- Ocular > 4, 14, 15
- Retinal infarction > 8, 11
- Other > 5, 9, 10, 12, 16, 17
We will also group as follows in AsymptSympt:
- Asymptomatic > 0
- TIA > 1, 7, 13 + Stroke > 2, 3
- Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17
We will also group as follows in AsymptSympt2G:
- Asymptomatic > 0
- TIA > 1, 7, 13 + Stroke > 2, 3 Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17
# Fix symptoms
attach(AEDB)
AEDB$sympt[is.na(AEDB$sympt)] <- -999
# Symptoms.5G
AEDB[,"Symptoms.5G"] <- NA
# AEDB$Symptoms.5G[sympt == "NA"] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == -999] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"
# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == -999] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"
# AsymptSympt
AEDB[,"AsymptSympt2G"] <- NA
AEDB$AsymptSympt2G[sympt == -999] <- NA
AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"
detach(AEDB)
# table(AEDB$sympt, useNA = "ifany")
# table(AEDB$AsymptSympt2G, useNA = "ifany")
# table(AEDB$Symptoms.5G, useNA = "ifany")
#
# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")
Asymptomatic Ocular Other Retinal infarction Stroke TIA <NA>
Asymptomatic 333 0 0 0 0 0 0
Symptomatic 0 417 119 43 733 1045 0
<NA> 0 0 0 0 0 0 1103# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
#
# rm(AEDB.temp)
We will also fix the plaquephenotypes variable.
Coding of symptoms is as follows:
- missing -999
- not relevant -888
- fibrous 1
- fibroatheromatous 2
- atheromatous 3
# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)
table(AEDB$OverallPlaquePhenotype)
atheromatous fibroatheromatous fibrous
550 843 1439 # AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
We will also fix the diabetes status variable. We define diabetes as history of a diagnosis and/or use of glucose-lowering medications.
# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)
table(AEDB$DM.composite)
0 1
2766 985 table(AEDB$DiabetesStatus)
Control (no Diabetes Dx/Med) Diabetes
2766 985 # AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
We will also fix the smoking status variable. We are interested in whether someone never, ever or is currently (at the time of inclusion) smoking. This is based on the questionnaire.
diet801: are you a smoker?diet802: did you smoke in the past?
We already have some variables indicating smoking status:
SmokingReported: patient has reported to smoke.SmokingYearOR: smoking in the year of surgery?SmokerCurrent: currently smoking?
require(labelled)
AEDB$diet801 <- to_factor(AEDB$diet801)
AEDB$diet802 <- to_factor(AEDB$diet802)
AEDB$diet805 <- to_factor(AEDB$diet805)
AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)
# table(AEDB$diet801)
# table(AEDB$diet802)
# table(AEDB$SmokingReported)
# table(AEDB$SmokerCurrent)
# table(AEDB$SmokingYearOR)
# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
#
# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))
cat("\nFixing smoking status.\n")
Fixing smoking status.attach(AEDB)
AEDB[,"SmokerStatus"] <- NA
AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
detach(AEDB)
cat("\n* Current smoking status.\n")
* Current smoking status.table(AEDB$SmokerCurrent,
useNA = "ifany",
dnn = c("Current smoker"))Current smoker
no data available/missing no yes <NA>
0 2364 1310 119 cat("\n* Updated smoking status.\n")
* Updated smoking status.table(AEDB$SmokerStatus,
useNA = "ifany",
dnn = c("Updated smoking status"))Updated smoking status
Current smoker Ex-smoker Never smoked <NA>
1310 1814 389 280 cat("\n* Comparing to 'SmokerCurrent'.\n")
* Comparing to 'SmokerCurrent'.table(AEDB$SmokerStatus, AEDB$SmokerCurrent,
useNA = "ifany",
dnn = c("Updated smoking status", "Current smoker")) Current smoker
Updated smoking status no data available/missing no yes <NA>
Current smoker 0 0 1310 0
Ex-smoker 0 1814 0 0
Never smoked 0 389 0 0
<NA> 0 161 0 119# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
We will also fix the alcohol status variable.
# Fix diabetes
attach(AEDB)
AEDB[,"AlcoholUse"] <- NA
AEDB$AlcoholUse[diet810 == -999] <- NA
AEDB$AlcoholUse[diet810 == 0] <- "No"
AEDB$AlcoholUse[diet810 == 1] <- "Yes"
detach(AEDB)
table(AEDB$AlcoholUse)
No Yes
1238 2346 # AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
We will also fix a history of CAD, stroke or peripheral intervention status variable. This will be based on CAD_history, Stroke_history, and Peripheral.interv
# Fix diabetes
attach(AEDB)
AEDB[,"MedHx_CVD"] <- NA
AEDB$MedHx_CVD[CAD_history == 0 | Stroke_history == 0 | Peripheral.interv == 0] <- "No"
AEDB$MedHx_CVD[CAD_history == 1 | Stroke_history == 1 | Peripheral.interv == 1] <- "yes"
detach(AEDB)
table(AEDB$CAD_history)
0 1
2432 1285 table(AEDB$Stroke_history)
0 1
2764 948 table(AEDB$Peripheral.interv)
0 1
2581 1099 table(AEDB$MedHx_CVD)
No yes
1310 2476 # AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
Athero-Express Biobank Study
Baseline characteristics
We are interested in the following variables at baseline.
- Age (years)
- Female sex (N, %)
- Hypertension (N, %)
- SBP (mmHg)
- DBP (mmHg)
- Diabetes mellitus (N, %)
- Total cholesterol levels (mg/dL)
- LDL cholesterol levels (mg/dL)
- HDL cholesterol levels (mg/dL)
- Triglyceride levels (mg/dL)
- Use of statins (N, %)
- Use of antiplatelet drugs (N, %)
- BMI (kg/m²)
- Smoking status (N, %)
- Never smokers
- Ex-smokers
- Current smokers
- History of CAD (N, %)
- History of PAD (N, %)
- Clinical manifestations
- Asymptomatic
- Amaurosis fugax
- TIA
- Stroke
- eGFR (mL/min/1.73 m²)
- MCP-1 plaque levels (pg/mL) (LUMINEX based, two experiments
MCP1, andMCP1_pg_ug_2015)
NOT AVAILABLE YET - MCP-1 plasma levels (pg/mL) (OLINK based)
cat("===========================================================================================\n")===========================================================================================cat("CREATE BASELINE TABLE\n")CREATE BASELINE TABLE# Baseline table variables
basetable_vars = c("Hospital", "ORyear",
"Age", "Gender",
"TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU",
"TC_final", "LDL_final", "HDL_final", "TG_final",
"hsCRP_plasma",
"systolic", "diastoli", "GFR_MDRD", "BMI",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"restenos", "stenose",
"MedHx_CVD", "CAD_history", "PAOD", "Peripheral.interv",
"EP_composite", "EP_composite_time",
"macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
"neutrophils", "Mast_cells_plaque",
"IPH.bin", "vessel_density_averaged",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
"IL6", "IL6_pg_ug_2015", "IL6R_pg_ug_2015",
"MCP1", "MCP1_pg_ug_2015", "MCP1_pg_ml_2015")
basetable_bin = c("Gender",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"restenos", "stenose",
"CAD_history", "PAOD", "Peripheral.interv",
"EP_composite", "Macrophages.bin", "SMC.bin",
"IPH.bin",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
# basetable_bin
basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_conAll patients
Showing the baseline table of the whole Athero-Express Biobank.
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
# strata = "Symptoms.4g",
data = AEDB, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:3]
level Overall Missing
n 3793
Hospital % (freq) St. Antonius, Nieuwegein 45.7 (1735) 0.0
UMC Utrecht 54.3 (2058)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0
2002 2.5 ( 94)
2003 5.4 ( 204)
2004 7.6 ( 289)
2005 8.1 ( 309)
2006 7.5 ( 285)
2007 6.2 ( 234)
2008 5.9 ( 223)
2009 7.1 ( 268)
2010 8.1 ( 307)
2011 7.1 ( 270)
2012 8.2 ( 312)
2013 6.9 ( 262)
2014 7.9 ( 299)
2015 2.1 ( 79)
2016 3.3 ( 124)
2017 2.2 ( 85)
2018 2.1 ( 80)
2019 1.8 ( 69)
Age (mean (SD)) 68.906 (9.322) 0.0
Gender % (freq) female 30.6 (1161) 0.0
male 69.4 (2632)
TC_finalCU (mean (SD)) 185.256 (81.509) 46.8
LDL_finalCU (mean (SD)) 106.533 (40.725) 54.5
HDL_finalCU (mean (SD)) 46.591 (16.725) 51.1
TG_finalCU (mean (SD)) 154.212 (99.774) 51.8
TC_final (mean (SD)) 4.798 (2.111) 46.8
LDL_final (mean (SD)) 2.759 (1.055) 54.5
HDL_final (mean (SD)) 1.207 (0.433) 51.1
TG_final (mean (SD)) 1.743 (1.127) 51.8
hsCRP_plasma (mean (SD)) 19.231 (206.750) 60.6
systolic (mean (SD)) 150.901 (25.114) 13.5
diastoli (mean (SD)) 79.933 (21.847) 13.5
GFR_MDRD (mean (SD)) 74.844 (24.740) 6.5
BMI (mean (SD)) 26.336 (4.050) 7.5
KDOQI % (freq) No data available/missing 0.0 ( 0) 6.6
Normal kidney function 22.1 ( 839)
CKD 2 (Mild) 47.2 (1789)
CKD 3 (Moderate) 21.9 ( 831)
CKD 4 (Severe) 1.4 ( 53)
CKD 5 (Failure) 0.8 ( 32)
<NA> 6.6 ( 249)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 7.5
Underweight 1.2 ( 44)
Normal 35.2 (1336)
Overweight 42.1 (1595)
Obese 14.1 ( 533)
<NA> 7.5 ( 285)
SmokerStatus % (freq) Current smoker 34.5 (1310) 7.4
Ex-smoker 47.8 (1814)
Never smoked 10.3 ( 389)
<NA> 7.4 ( 280)
AlcoholUse % (freq) No 32.6 (1238) 5.5
Yes 61.9 (2346)
<NA> 5.5 ( 209)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 72.9 (2766) 1.1
Diabetes 26.0 ( 985)
<NA> 1.1 ( 42)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 4.0
no 23.7 ( 900)
yes 72.3 (2742)
<NA> 4.0 ( 151)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 5.5
no 28.6 (1086)
yes 65.9 (2500)
<NA> 5.5 ( 207)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 1.3
no 13.3 ( 505)
yes 85.4 (3240)
<NA> 1.3 ( 48)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 1.5
no 21.0 ( 798)
yes 77.5 (2940)
<NA> 1.5 ( 55)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 1.6
no 85.6 (3248)
yes 12.8 ( 485)
<NA> 1.6 ( 60)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 1.6
no 13.7 ( 521)
yes 84.7 (3213)
<NA> 1.6 ( 59)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 1.5
no 21.8 ( 826)
yes 76.7 (2911)
<NA> 1.5 ( 56)
Stroke_Dx % (freq) Missing 0.0 ( 0) 8.1
No stroke diagnosed 74.4 (2823)
Stroke diagnosed 17.5 ( 663)
<NA> 8.1 ( 307)
sympt % (freq) missing 29.1 (1103) 0.0
Asymptomatic 8.8 ( 333)
TIA 27.4 (1040)
minor stroke 12.1 ( 458)
Major stroke 7.3 ( 275)
Amaurosis fugax 10.5 ( 399)
Four vessel disease 1.1 ( 43)
Vertebrobasilary TIA 0.1 ( 5)
Retinal infarction 1.0 ( 37)
Symptomatic, but aspecific symtoms 1.6 ( 61)
Contralateral symptomatic occlusion 0.3 ( 12)
retinal infarction 0.2 ( 6)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 1)
retinal infarction + TIAs 0.0 ( 0)
Ocular ischemic syndrome 0.5 ( 18)
ischemisch glaucoom 0.0 ( 0)
subclavian steal syndrome 0.1 ( 2)
TGA 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 8.8 ( 333) 29.1
Ocular 11.0 ( 417)
Other 3.1 ( 119)
Retinal infarction 1.1 ( 43)
Stroke 19.3 ( 733)
TIA 27.6 (1045)
<NA> 29.1 (1103)
AsymptSympt % (freq) Asymptomatic 8.8 ( 333) 29.1
Ocular and others 15.3 ( 579)
Symptomatic 46.9 (1778)
<NA> 29.1 (1103)
AsymptSympt2G % (freq) Asymptomatic 8.8 ( 333) 29.1
Symptomatic 62.1 (2357)
<NA> 29.1 (1103)
restenos % (freq) missing 0.0 ( 0) 4.0
de novo 87.0 (3299)
restenosis 8.8 ( 334)
stenose bij angioseal na PTCA 0.2 ( 7)
<NA> 4.0 ( 153)
stenose % (freq) missing 0.0 ( 0) 7.0
0-49% 0.7 ( 25)
50-70% 6.8 ( 257)
70-90% 35.6 (1349)
90-99% 29.9 (1133)
100% (Occlusion) 14.8 ( 560)
NA 0.1 ( 3)
50-99% 2.6 ( 99)
70-99% 2.6 ( 100)
99 0.1 ( 2)
<NA> 7.0 ( 265)
MedHx_CVD % (freq) No 34.5 (1310) 0.2
yes 65.3 (2476)
<NA> 0.2 ( 7)
CAD_history % (freq) Missing 0.0 ( 0) 2.0
No history CAD 64.1 (2432)
History CAD 33.9 (1285)
<NA> 2.0 ( 76)
PAOD % (freq) missing/no data 0.0 ( 0) 1.6
no 55.1 (2090)
yes 43.3 (1644)
<NA> 1.6 ( 59)
Peripheral.interv % (freq) no 68.0 (2581) 3.0
yes 29.0 (1099)
<NA> 3.0 ( 113)
EP_composite % (freq) No data available. 0.0 ( 0) 7.3
No composite endpoints 60.6 (2299)
Composite endpoints 32.1 (1218)
<NA> 7.3 ( 276)
EP_composite_time (mean (SD)) 2.267 (1.203) 7.4
macmean0 (mean (SD)) 0.656 (1.154) 32.4
smcmean0 (mean (SD)) 2.292 (6.618) 32.4
Macrophages.bin % (freq) no/minor 42.3 (1603) 25.7
moderate/heavy 32.1 (1216)
<NA> 25.7 ( 974)
SMC.bin % (freq) no/minor 22.9 ( 870) 25.3
moderate/heavy 51.8 (1964)
<NA> 25.3 ( 959)
neutrophils (mean (SD)) 162.985 (490.469) 91.0
Mast_cells_plaque (mean (SD)) 165.663 (163.421) 93.0
IPH.bin % (freq) no 32.3 (1225) 24.8
yes 42.9 (1628)
<NA> 24.8 ( 940)
vessel_density_averaged (mean (SD)) 8.030 (6.344) 48.0
Calc.bin % (freq) no/minor 37.9 (1438) 24.7
moderate/heavy 37.4 (1417)
<NA> 24.7 ( 938)
Collagen.bin % (freq) no/minor 14.2 ( 540) 25.2
moderate/heavy 60.6 (2299)
<NA> 25.2 ( 954)
Fat.bin_10 % (freq) <10% 32.3 (1226) 24.7
>10% 43.0 (1630)
<NA> 24.7 ( 937)
Fat.bin_40 % (freq) <40% 60.0 (2276) 24.7
>40% 15.3 ( 580)
<NA> 24.7 ( 937)
OverallPlaquePhenotype % (freq) atheromatous 14.5 ( 550) 25.3
fibroatheromatous 22.2 ( 843)
fibrous 37.9 (1439)
<NA> 25.3 ( 961)
IL6 (mean (SD)) 94.451 (278.490) 84.5
IL6_pg_ug_2015 (mean (SD)) 0.134 (0.541) 67.2
IL6R_pg_ug_2015 (mean (SD)) 0.211 (0.251) 67.1
MCP1 (mean (SD)) 130.926 (118.422) 83.7
MCP1_pg_ug_2015 (mean (SD)) 0.596 (0.879) 65.4
MCP1_pg_ml_2015 (mean (SD)) 587.541 (843.110) 65.3 CEA patients
Showing the baseline table of the CEA patients in the Athero-Express Biobank.
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
# strata = "Symptoms.4g",
data = AEDB.CEA, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:3]
level Overall Missing
n 2423
Hospital % (freq) St. Antonius, Nieuwegein 39.1 ( 948) 0.0
UMC Utrecht 60.9 (1475)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0
2002 3.3 ( 81)
2003 6.5 ( 157)
2004 7.8 ( 190)
2005 7.6 ( 185)
2006 7.6 ( 183)
2007 6.3 ( 152)
2008 5.7 ( 138)
2009 7.5 ( 182)
2010 6.6 ( 159)
2011 6.8 ( 164)
2012 7.3 ( 176)
2013 6.1 ( 149)
2014 6.7 ( 163)
2015 3.1 ( 76)
2016 3.5 ( 85)
2017 2.7 ( 65)
2018 2.7 ( 66)
2019 2.1 ( 52)
Age (mean (SD)) 69.103 (9.302) 0.0
Gender % (freq) female 30.5 ( 739) 0.0
male 69.5 (1684)
TC_finalCU (mean (SD)) 184.852 (56.275) 38.0
LDL_finalCU (mean (SD)) 108.484 (41.794) 45.6
HDL_finalCU (mean (SD)) 46.432 (16.999) 41.7
TG_finalCU (mean (SD)) 151.189 (91.249) 42.8
TC_final (mean (SD)) 4.788 (1.458) 38.0
LDL_final (mean (SD)) 2.810 (1.082) 45.6
HDL_final (mean (SD)) 1.203 (0.440) 41.7
TG_final (mean (SD)) 1.708 (1.031) 42.8
hsCRP_plasma (mean (SD)) 19.887 (231.453) 52.9
systolic (mean (SD)) 152.408 (25.163) 11.3
diastoli (mean (SD)) 81.314 (25.178) 11.3
GFR_MDRD (mean (SD)) 73.115 (21.145) 5.4
BMI (mean (SD)) 26.488 (3.976) 5.9
KDOQI % (freq) No data available/missing 0.0 ( 0) 5.4
Normal kidney function 19.1 ( 462)
CKD 2 (Mild) 50.9 (1233)
CKD 3 (Moderate) 22.9 ( 554)
CKD 4 (Severe) 1.3 ( 32)
CKD 5 (Failure) 0.4 ( 10)
<NA> 5.4 ( 132)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 5.9
Underweight 1.0 ( 24)
Normal 35.1 ( 851)
Overweight 43.4 (1052)
Obese 14.5 ( 352)
<NA> 5.9 ( 144)
SmokerStatus % (freq) Current smoker 33.2 ( 805) 5.9
Ex-smoker 48.0 (1163)
Never smoked 12.9 ( 313)
<NA> 5.9 ( 142)
AlcoholUse % (freq) No 34.5 ( 835) 4.1
Yes 61.5 (1489)
<NA> 4.1 ( 99)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 75.2 (1822) 1.1
Diabetes 23.7 ( 574)
<NA> 1.1 ( 27)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 3.2
no 24.3 ( 590)
yes 72.4 (1755)
<NA> 3.2 ( 78)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 4.4
no 30.0 ( 726)
yes 65.6 (1590)
<NA> 4.4 ( 107)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 1.2
no 14.6 ( 354)
yes 84.2 (2041)
<NA> 1.2 ( 28)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 1.4
no 23.4 ( 566)
yes 75.3 (1824)
<NA> 1.4 ( 33)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 1.6
no 87.3 (2116)
yes 11.1 ( 269)
<NA> 1.6 ( 38)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 1.5
no 12.2 ( 295)
yes 86.3 (2092)
<NA> 1.5 ( 36)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 1.4
no 20.3 ( 491)
yes 78.3 (1898)
<NA> 1.4 ( 34)
Stroke_Dx % (freq) Missing 0.0 ( 0) 6.9
No stroke diagnosed 71.5 (1732)
Stroke diagnosed 21.7 ( 525)
<NA> 6.9 ( 166)
sympt % (freq) missing 0.0 ( 0) 0.0
Asymptomatic 11.1 ( 270)
TIA 39.7 ( 961)
minor stroke 16.8 ( 407)
Major stroke 9.9 ( 239)
Amaurosis fugax 15.7 ( 380)
Four vessel disease 1.6 ( 38)
Vertebrobasilary TIA 0.2 ( 5)
Retinal infarction 1.4 ( 34)
Symptomatic, but aspecific symtoms 2.2 ( 53)
Contralateral symptomatic occlusion 0.5 ( 11)
retinal infarction 0.2 ( 6)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 1)
retinal infarction + TIAs 0.0 ( 0)
Ocular ischemic syndrome 0.7 ( 16)
ischemisch glaucoom 0.0 ( 0)
subclavian steal syndrome 0.1 ( 2)
TGA 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 11.1 ( 270) 0.0
Ocular 16.3 ( 396)
Other 4.3 ( 105)
Retinal infarction 1.7 ( 40)
Stroke 26.7 ( 646)
TIA 39.9 ( 966)
AsymptSympt % (freq) Asymptomatic 11.1 ( 270) 0.0
Ocular and others 22.3 ( 541)
Symptomatic 66.5 (1612)
AsymptSympt2G % (freq) Asymptomatic 11.1 ( 270) 0.0
Symptomatic 88.9 (2153)
restenos % (freq) missing 0.0 ( 0) 1.4
de novo 93.7 (2270)
restenosis 4.9 ( 118)
stenose bij angioseal na PTCA 0.0 ( 0)
<NA> 1.4 ( 35)
stenose % (freq) missing 0.0 ( 0) 2.0
0-49% 0.5 ( 13)
50-70% 7.8 ( 190)
70-90% 46.5 (1127)
90-99% 38.3 ( 928)
100% (Occlusion) 1.3 ( 31)
NA 0.0 ( 1)
50-99% 0.6 ( 15)
70-99% 2.8 ( 68)
99 0.1 ( 2)
<NA> 2.0 ( 48)
MedHx_CVD % (freq) No 36.9 ( 893) 0.0
yes 63.1 (1530)
CAD_history % (freq) Missing 0.0 ( 0) 1.9
No history CAD 66.9 (1620)
History CAD 31.2 ( 756)
<NA> 1.9 ( 47)
PAOD % (freq) missing/no data 0.0 ( 0) 2.0
no 77.5 (1878)
yes 20.5 ( 497)
<NA> 2.0 ( 48)
Peripheral.interv % (freq) no 77.2 (1870) 2.9
yes 19.9 ( 482)
<NA> 2.9 ( 71)
EP_composite % (freq) No data available. 0.0 ( 0) 5.0
No composite endpoints 70.6 (1711)
Composite endpoints 24.3 ( 590)
<NA> 5.0 ( 122)
EP_composite_time (mean (SD)) 2.479 (1.109) 5.2
macmean0 (mean (SD)) 0.767 (1.183) 29.7
smcmean0 (mean (SD)) 1.985 (2.380) 29.9
Macrophages.bin % (freq) no/minor 35.0 ( 847) 24.1
moderate/heavy 40.9 ( 992)
<NA> 24.1 ( 584)
SMC.bin % (freq) no/minor 24.8 ( 602) 23.8
moderate/heavy 51.3 (1244)
<NA> 23.8 ( 577)
neutrophils (mean (SD)) 147.151 (419.998) 87.5
Mast_cells_plaque (mean (SD)) 164.488 (163.771) 90.0
IPH.bin % (freq) no 30.8 ( 746) 23.5
yes 45.7 (1108)
<NA> 23.5 ( 569)
vessel_density_averaged (mean (SD)) 8.317 (6.384) 35.1
Calc.bin % (freq) no/minor 41.6 (1007) 23.4
moderate/heavy 35.1 ( 850)
<NA> 23.4 ( 566)
Collagen.bin % (freq) no/minor 15.8 ( 382) 23.6
moderate/heavy 60.6 (1469)
<NA> 23.6 ( 572)
Fat.bin_10 % (freq) <10% 22.4 ( 542) 23.3
>10% 54.3 (1316)
<NA> 23.3 ( 565)
Fat.bin_40 % (freq) <40% 56.2 (1362) 23.3
>40% 20.5 ( 496)
<NA> 23.3 ( 565)
OverallPlaquePhenotype % (freq) atheromatous 19.8 ( 480) 23.7
fibroatheromatous 27.8 ( 674)
fibrous 28.7 ( 695)
<NA> 23.7 ( 574)
IL6 (mean (SD)) 98.812 (292.457) 78.2
IL6_pg_ug_2015 (mean (SD)) 0.138 (0.556) 52.5
IL6R_pg_ug_2015 (mean (SD)) 0.211 (0.251) 52.4
MCP1 (mean (SD)) 135.763 (120.028) 76.7
MCP1_pg_ug_2015 (mean (SD)) 0.612 (0.904) 50.6
MCP1_pg_ml_2015 (mean (SD)) 600.444 (858.416) 50.5 CEA patients with MCP1_pg_ug_2015
Showing the baseline table of the CEA patients in the Athero-Express Biobank with MCP1_pg_ug_2015.
AEDB.CEA.subset <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015))
AEDB.CEA.subset.AsymptSympt.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
strata = "AsymptSympt2G",
data = AEDB.CEA.subset, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:6] Stratified by AsymptSympt2G
level Asymptomatic Symptomatic p test Missing
n 131 1067
Hospital % (freq) St. Antonius, Nieuwegein 50.4 ( 66) 46.5 ( 496) 0.453 0.0
UMC Utrecht 49.6 ( 65) 53.5 ( 571)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
2002 10.7 ( 14) 3.9 ( 42)
2003 7.6 ( 10) 9.4 ( 100)
2004 17.6 ( 23) 11.5 ( 123)
2005 9.9 ( 13) 11.2 ( 119)
2006 10.7 ( 14) 10.2 ( 109)
2007 11.5 ( 15) 10.5 ( 112)
2008 7.6 ( 10) 7.4 ( 79)
2009 7.6 ( 10) 8.4 ( 90)
2010 5.3 ( 7) 7.6 ( 81)
2011 6.1 ( 8) 9.6 ( 102)
2012 5.3 ( 7) 8.2 ( 88)
2013 0.0 ( 0) 2.0 ( 21)
2014 0.0 ( 0) 0.1 ( 1)
2015 0.0 ( 0) 0.0 ( 0)
2016 0.0 ( 0) 0.0 ( 0)
2017 0.0 ( 0) 0.0 ( 0)
2018 0.0 ( 0) 0.0 ( 0)
2019 0.0 ( 0) 0.0 ( 0)
Age (mean (SD)) 66.237 (9.184) 68.937 (9.119) 0.001 0.0
Gender % (freq) female 23.7 ( 31) 31.4 ( 335) 0.087 0.0
male 76.3 (100) 68.6 ( 732)
TC_finalCU (mean (SD)) 175.987 (47.184) 183.526 (48.426) 0.174 33.5
LDL_finalCU (mean (SD)) 102.781 (38.324) 109.377 (41.109) 0.183 39.6
HDL_finalCU (mean (SD)) 43.701 (14.754) 45.809 (18.513) 0.318 36.4
TG_finalCU (mean (SD)) 157.650 (89.246) 145.194 (84.818) 0.209 36.1
TC_final (mean (SD)) 4.558 (1.222) 4.753 (1.254) 0.174 33.5
LDL_final (mean (SD)) 2.662 (0.993) 2.833 (1.065) 0.183 39.6
HDL_final (mean (SD)) 1.132 (0.382) 1.186 (0.479) 0.318 36.4
TG_final (mean (SD)) 1.781 (1.008) 1.641 (0.958) 0.209 36.1
hsCRP_plasma (mean (SD)) 5.688 (19.440) 16.551 (113.708) 0.380 38.7
systolic (mean (SD)) 153.577 (24.327) 155.790 (26.176) 0.397 13.9
diastoli (mean (SD)) 80.622 (13.225) 82.883 (13.573) 0.097 13.9
GFR_MDRD (mean (SD)) 71.026 (20.424) 71.866 (20.055) 0.658 3.5
BMI (mean (SD)) 26.623 (3.391) 26.320 (3.745) 0.381 4.2
KDOQI % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 3.6
Normal kidney function 17.6 ( 23) 17.2 ( 184)
CKD 2 (Mild) 49.6 ( 65) 53.2 ( 568)
CKD 3 (Moderate) 28.2 ( 37) 24.4 ( 260)
CKD 4 (Severe) 0.0 ( 0) 1.2 ( 13)
CKD 5 (Failure) 0.8 ( 1) 0.4 ( 4)
<NA> 3.8 ( 5) 3.6 ( 38)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 4.3
Underweight 0.8 ( 1) 0.9 ( 10)
Normal 32.8 ( 43) 35.6 ( 380)
Overweight 51.1 ( 67) 46.1 ( 492)
Obese 13.0 ( 17) 12.7 ( 136)
<NA> 2.3 ( 3) 4.6 ( 49)
SmokerStatus % (freq) Current smoker 30.5 ( 40) 36.3 ( 387) 0.075 3.8
Ex-smoker 57.3 ( 75) 45.5 ( 486)
Never smoked 9.9 ( 13) 14.2 ( 152)
<NA> 2.3 ( 3) 3.9 ( 42)
AlcoholUse % (freq) No 38.2 ( 50) 33.3 ( 355) 0.342 4.1
Yes 59.5 ( 78) 62.4 ( 666)
<NA> 2.3 ( 3) 4.3 ( 46)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 76.3 (100) 77.4 ( 826) 0.867 0.0
Diabetes 23.7 ( 31) 22.6 ( 241)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 2.0
no 23.7 ( 31) 26.6 ( 284)
yes 75.6 ( 99) 71.2 ( 760)
<NA> 0.8 ( 1) 2.2 ( 23)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 2.7
no 30.5 ( 40) 32.9 ( 351)
yes 67.9 ( 89) 64.3 ( 686)
<NA> 1.5 ( 2) 2.8 ( 30)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
no 9.9 ( 13) 14.3 ( 153)
yes 90.1 (118) 85.7 ( 914)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 14.5 ( 19) 23.3 ( 249)
yes 85.5 (112) 76.5 ( 816)
<NA> 0.0 ( 0) 0.2 ( 2)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 89.3 (117) 88.0 ( 939)
yes 10.7 ( 14) 11.8 ( 126)
<NA> 0.0 ( 0) 0.2 ( 2)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.4
no 6.1 ( 8) 11.0 ( 117)
yes 93.1 (122) 88.7 ( 946)
<NA> 0.8 ( 1) 0.4 ( 4)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 15.3 ( 20) 22.7 ( 242)
yes 84.7 (111) 77.1 ( 823)
<NA> 0.0 ( 0) 0.2 ( 2)
Stroke_Dx % (freq) Missing 0.0 ( 0) 0.0 ( 0) NaN 5.3
No stroke diagnosed 80.2 (105) 75.2 ( 802)
Stroke diagnosed 14.5 ( 19) 19.5 ( 208)
<NA> 5.3 ( 7) 5.3 ( 57)
sympt % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
Asymptomatic 100.0 (131) 0.0 ( 0)
TIA 0.0 ( 0) 46.3 ( 494)
minor stroke 0.0 ( 0) 16.7 ( 178)
Major stroke 0.0 ( 0) 12.3 ( 131)
Amaurosis fugax 0.0 ( 0) 17.2 ( 184)
Four vessel disease 0.0 ( 0) 2.2 ( 23)
Vertebrobasilary TIA 0.0 ( 0) 0.2 ( 2)
Retinal infarction 0.0 ( 0) 1.4 ( 15)
Symptomatic, but aspecific symtoms 0.0 ( 0) 2.7 ( 29)
Contralateral symptomatic occlusion 0.0 ( 0) 0.6 ( 6)
retinal infarction 0.0 ( 0) 0.3 ( 3)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 0) 0.1 ( 1)
retinal infarction + TIAs 0.0 ( 0) 0.0 ( 0)
Ocular ischemic syndrome 0.0 ( 0) 0.1 ( 1)
ischemisch glaucoom 0.0 ( 0) 0.0 ( 0)
subclavian steal syndrome 0.0 ( 0) 0.0 ( 0)
TGA 0.0 ( 0) 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 100.0 (131) 0.0 ( 0) <0.001 0.0
Ocular 0.0 ( 0) 17.3 ( 185)
Other 0.0 ( 0) 5.5 ( 59)
Retinal infarction 0.0 ( 0) 1.7 ( 18)
Stroke 0.0 ( 0) 29.0 ( 309)
TIA 0.0 ( 0) 46.5 ( 496)
AsymptSympt % (freq) Asymptomatic 100.0 (131) 0.0 ( 0) <0.001 0.0
Ocular and others 0.0 ( 0) 24.6 ( 262)
Symptomatic 0.0 ( 0) 75.4 ( 805)
AsymptSympt2G % (freq) Asymptomatic 100.0 (131) 0.0 ( 0) <0.001 0.0
Symptomatic 0.0 ( 0) 100.0 (1067)
restenos % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 2.3
de novo 93.9 (123) 94.8 (1011)
restenosis 2.3 ( 3) 3.2 ( 34)
stenose bij angioseal na PTCA 0.0 ( 0) 0.0 ( 0)
<NA> 3.8 ( 5) 2.1 ( 22)
stenose % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 3.2
0-49% 0.0 ( 0) 0.6 ( 6)
50-70% 3.1 ( 4) 6.5 ( 69)
70-90% 51.1 ( 67) 44.5 ( 475)
90-99% 41.2 ( 54) 42.7 ( 456)
100% (Occlusion) 0.0 ( 0) 0.9 ( 10)
NA 0.0 ( 0) 0.0 ( 0)
50-99% 0.8 ( 1) 0.4 ( 4)
70-99% 0.0 ( 0) 1.3 ( 14)
99 0.0 ( 0) 0.0 ( 0)
<NA> 3.8 ( 5) 3.1 ( 33)
MedHx_CVD % (freq) No 38.9 ( 51) 36.9 ( 394) 0.724 0.0
yes 61.1 ( 80) 63.1 ( 673)
CAD_history % (freq) Missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
No history CAD 61.8 ( 81) 69.9 ( 746)
History CAD 38.2 ( 50) 30.1 ( 321)
PAOD % (freq) missing/no data 0.0 ( 0) 0.0 ( 0) NaN 0.0
no 74.0 ( 97) 79.6 ( 849)
yes 26.0 ( 34) 20.4 ( 218)
Peripheral.interv % (freq) no 74.0 ( 97) 82.6 ( 881) 0.041 0.3
yes 26.0 ( 34) 17.2 ( 183)
<NA> 0.0 ( 0) 0.3 ( 3)
EP_composite % (freq) No data available. 0.0 ( 0) 0.0 ( 0) NaN 0.8
No composite endpoints 67.2 ( 88) 74.3 ( 793)
Composite endpoints 32.8 ( 43) 24.8 ( 265)
<NA> 0.0 ( 0) 0.8 ( 9)
EP_composite_time (mean (SD)) 2.614 (0.931) 2.614 (1.094) 0.998 0.9
macmean0 (mean (SD)) 0.837 (1.088) 0.780 (1.230) 0.618 2.3
smcmean0 (mean (SD)) 2.152 (1.861) 1.905 (2.221) 0.225 2.7
Macrophages.bin % (freq) no/minor 48.9 ( 64) 47.4 ( 506) 0.584 1.9
moderate/heavy 50.4 ( 66) 50.5 ( 539)
<NA> 0.8 ( 1) 2.1 ( 22)
SMC.bin % (freq) no/minor 22.9 ( 30) 32.1 ( 343) 0.087 1.8
moderate/heavy 75.6 ( 99) 66.0 ( 704)
<NA> 1.5 ( 2) 1.9 ( 20)
neutrophils (mean (SD)) 157.643 (507.380) 172.872 (477.038) 0.876 82.0
Mast_cells_plaque (mean (SD)) 111.400 (112.037) 183.284 (180.156) 0.056 86.1
IPH.bin % (freq) no 41.2 ( 54) 38.1 ( 406) 0.571 1.7
yes 58.0 ( 76) 60.2 ( 642)
<NA> 0.8 ( 1) 1.8 ( 19)
vessel_density_averaged (mean (SD)) 8.608 (6.547) 8.406 (6.463) 0.748 8.7
[ reached getOption("max.print") -- omitted 22 rows ]CEA patients with MCP1_pg_ug_2015 and MCP1
Showing the baseline table of the CEA patients in the Athero-Express Biobank with MCP1_pg_ug_2015 and MCP1.
AEDB.CEA.subset.combo <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) | !is.na(MCP1))
AEDB.CEA.subset.combo.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
strata = "AsymptSympt2G",
data = AEDB.CEA.subset.combo, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:6] Stratified by AsymptSympt2G
level Asymptomatic Symptomatic p test Missing
n 161 1167
Hospital % (freq) St. Antonius, Nieuwegein 52.2 ( 84) 46.9 ( 547) 0.239 0.0
UMC Utrecht 47.8 ( 77) 53.1 ( 620)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
2002 10.6 ( 17) 4.8 ( 56)
2003 11.8 ( 19) 10.6 ( 124)
2004 19.9 ( 32) 12.2 ( 142)
2005 13.7 ( 22) 13.3 ( 155)
2006 8.7 ( 14) 9.9 ( 116)
2007 9.3 ( 15) 9.6 ( 112)
2008 6.2 ( 10) 6.8 ( 79)
2009 6.2 ( 10) 7.7 ( 90)
2010 4.3 ( 7) 6.9 ( 81)
2011 5.0 ( 8) 8.7 ( 102)
2012 4.3 ( 7) 7.5 ( 88)
2013 0.0 ( 0) 1.8 ( 21)
2014 0.0 ( 0) 0.1 ( 1)
2015 0.0 ( 0) 0.0 ( 0)
2016 0.0 ( 0) 0.0 ( 0)
2017 0.0 ( 0) 0.0 ( 0)
2018 0.0 ( 0) 0.0 ( 0)
2019 0.0 ( 0) 0.0 ( 0)
Age (mean (SD)) 65.901 (9.051) 68.785 (9.081) <0.001 0.0
Gender % (freq) female 23.0 ( 37) 30.4 ( 355) 0.065 0.0
male 77.0 (124) 69.6 ( 812)
TC_finalCU (mean (SD)) 179.199 (45.274) 184.078 (48.333) 0.322 32.8
LDL_finalCU (mean (SD)) 104.132 (37.590) 109.761 (41.318) 0.206 39.8
HDL_finalCU (mean (SD)) 44.749 (14.890) 45.803 (18.219) 0.570 36.1
TG_finalCU (mean (SD)) 158.699 (87.584) 145.901 (83.176) 0.141 35.6
TC_final (mean (SD)) 4.641 (1.173) 4.768 (1.252) 0.322 32.8
LDL_final (mean (SD)) 2.697 (0.974) 2.843 (1.070) 0.206 39.8
HDL_final (mean (SD)) 1.159 (0.386) 1.186 (0.472) 0.570 36.1
TG_final (mean (SD)) 1.793 (0.990) 1.649 (0.940) 0.141 35.6
hsCRP_plasma (mean (SD)) 6.846 (21.838) 16.179 (110.739) 0.394 40.6
systolic (mean (SD)) 152.838 (24.600) 155.713 (26.406) 0.230 13.5
diastoli (mean (SD)) 80.824 (12.855) 82.863 (13.542) 0.097 13.5
GFR_MDRD (mean (SD)) 70.440 (19.793) 71.890 (20.127) 0.400 3.5
BMI (mean (SD)) 26.626 (3.572) 26.350 (3.765) 0.387 4.4
KDOQI % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 3.5
Normal kidney function 14.9 ( 24) 17.4 ( 203)
CKD 2 (Mild) 50.9 ( 82) 53.4 ( 623)
CKD 3 (Moderate) 29.8 ( 48) 24.0 ( 280)
CKD 4 (Severe) 0.0 ( 0) 1.3 ( 15)
CKD 5 (Failure) 0.6 ( 1) 0.4 ( 5)
<NA> 3.7 ( 6) 3.5 ( 41)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 4.6
Underweight 1.2 ( 2) 0.9 ( 11)
Normal 32.3 ( 52) 35.6 ( 415)
Overweight 49.7 ( 80) 45.6 ( 532)
Obese 13.7 ( 22) 13.1 ( 153)
<NA> 3.1 ( 5) 4.8 ( 56)
SmokerStatus % (freq) Current smoker 29.2 ( 47) 36.1 ( 421) 0.069 4.0
Ex-smoker 56.5 ( 91) 45.6 ( 532)
Never smoked 11.8 ( 19) 14.1 ( 165)
<NA> 2.5 ( 4) 4.2 ( 49)
AlcoholUse % (freq) No 38.5 ( 62) 33.6 ( 392) 0.210 3.9
Yes 59.6 ( 96) 62.2 ( 726)
<NA> 1.9 ( 3) 4.2 ( 49)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 78.3 (126) 77.3 ( 902) 0.861 0.0
Diabetes 21.7 ( 35) 22.7 ( 265)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 1.9
no 25.5 ( 41) 26.6 ( 310)
yes 73.9 (119) 71.4 ( 833)
<NA> 0.6 ( 1) 2.1 ( 24)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 2.4
no 32.3 ( 52) 32.9 ( 384)
yes 66.5 (107) 64.5 ( 753)
<NA> 1.2 ( 2) 2.6 ( 30)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
no 11.2 ( 18) 14.1 ( 165)
yes 88.8 (143) 85.9 (1002)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 15.5 ( 25) 22.8 ( 266)
yes 83.9 (135) 77.0 ( 899)
<NA> 0.6 ( 1) 0.2 ( 2)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 89.4 (144) 88.0 (1027)
yes 9.9 ( 16) 11.8 ( 138)
<NA> 0.6 ( 1) 0.2 ( 2)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.5
no 6.2 ( 10) 10.8 ( 126)
yes 92.5 (149) 88.9 (1037)
<NA> 1.2 ( 2) 0.3 ( 4)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0) NaN 0.2
no 17.4 ( 28) 23.1 ( 270)
yes 82.0 (132) 76.7 ( 895)
<NA> 0.6 ( 1) 0.2 ( 2)
Stroke_Dx % (freq) Missing 0.0 ( 0) 0.0 ( 0) NaN 5.5
No stroke diagnosed 80.1 (129) 75.5 ( 881)
Stroke diagnosed 13.7 ( 22) 19.1 ( 223)
<NA> 6.2 ( 10) 5.4 ( 63)
sympt % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
Asymptomatic 100.0 (161) 0.0 ( 0)
TIA 0.0 ( 0) 46.5 ( 543)
minor stroke 0.0 ( 0) 17.1 ( 200)
Major stroke 0.0 ( 0) 11.7 ( 136)
Amaurosis fugax 0.0 ( 0) 17.0 ( 198)
Four vessel disease 0.0 ( 0) 2.1 ( 25)
Vertebrobasilary TIA 0.0 ( 0) 0.2 ( 2)
Retinal infarction 0.0 ( 0) 1.4 ( 16)
Symptomatic, but aspecific symtoms 0.0 ( 0) 3.1 ( 36)
Contralateral symptomatic occlusion 0.0 ( 0) 0.5 ( 6)
retinal infarction 0.0 ( 0) 0.3 ( 3)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 0) 0.1 ( 1)
retinal infarction + TIAs 0.0 ( 0) 0.0 ( 0)
Ocular ischemic syndrome 0.0 ( 0) 0.1 ( 1)
ischemisch glaucoom 0.0 ( 0) 0.0 ( 0)
subclavian steal syndrome 0.0 ( 0) 0.0 ( 0)
TGA 0.0 ( 0) 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 100.0 (161) 0.0 ( 0) <0.001 0.0
Ocular 0.0 ( 0) 17.1 ( 199)
Other 0.0 ( 0) 5.8 ( 68)
Retinal infarction 0.0 ( 0) 1.6 ( 19)
Stroke 0.0 ( 0) 28.8 ( 336)
TIA 0.0 ( 0) 46.7 ( 545)
AsymptSympt % (freq) Asymptomatic 100.0 (161) 0.0 ( 0) <0.001 0.0
Ocular and others 0.0 ( 0) 24.5 ( 286)
Symptomatic 0.0 ( 0) 75.5 ( 881)
AsymptSympt2G % (freq) Asymptomatic 100.0 (161) 0.0 ( 0) <0.001 0.0
Symptomatic 0.0 ( 0) 100.0 (1167)
restenos % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 2.0
de novo 93.2 (150) 95.0 (1109)
restenosis 3.7 ( 6) 3.1 ( 36)
stenose bij angioseal na PTCA 0.0 ( 0) 0.0 ( 0)
<NA> 3.1 ( 5) 1.9 ( 22)
stenose % (freq) missing 0.0 ( 0) 0.0 ( 0) NaN 2.9
0-49% 0.0 ( 0) 0.6 ( 7)
50-70% 2.5 ( 4) 6.3 ( 73)
70-90% 50.9 ( 82) 44.6 ( 520)
90-99% 42.9 ( 69) 43.3 ( 505)
100% (Occlusion) 0.0 ( 0) 0.9 ( 11)
NA 0.0 ( 0) 0.0 ( 0)
50-99% 0.6 ( 1) 0.3 ( 4)
70-99% 0.0 ( 0) 1.2 ( 14)
99 0.0 ( 0) 0.0 ( 0)
<NA> 3.1 ( 5) 2.8 ( 33)
MedHx_CVD % (freq) No 37.3 ( 60) 36.8 ( 429) 0.970 0.0
yes 62.7 (101) 63.2 ( 738)
CAD_history % (freq) Missing 0.0 ( 0) 0.0 ( 0) NaN 0.0
No history CAD 59.0 ( 95) 69.2 ( 807)
History CAD 41.0 ( 66) 30.8 ( 360)
PAOD % (freq) missing/no data 0.0 ( 0) 0.0 ( 0) NaN 0.0
no 73.9 (119) 79.9 ( 932)
yes 26.1 ( 42) 20.1 ( 235)
Peripheral.interv % (freq) no 72.7 (117) 83.0 ( 969) 0.004 0.2
yes 27.3 ( 44) 16.7 ( 195)
<NA> 0.0 ( 0) 0.3 ( 3)
EP_composite % (freq) No data available. 0.0 ( 0) 0.0 ( 0) NaN 0.8
No composite endpoints 68.3 (110) 73.9 ( 862)
Composite endpoints 31.7 ( 51) 25.2 ( 294)
<NA> 0.0 ( 0) 0.9 ( 11)
EP_composite_time (mean (SD)) 2.579 (0.961) 2.612 (1.129) 0.729 1.0
macmean0 (mean (SD)) 0.802 (1.072) 0.821 (1.275) 0.862 2.2
smcmean0 (mean (SD)) 2.445 (2.594) 1.924 (2.233) 0.007 2.5
Macrophages.bin % (freq) no/minor 50.3 ( 81) 45.8 ( 534) 0.311 1.8
moderate/heavy 49.1 ( 79) 52.3 ( 610)
<NA> 0.6 ( 1) 2.0 ( 23)
SMC.bin % (freq) no/minor 21.7 ( 35) 32.5 ( 379) 0.018 1.7
moderate/heavy 77.0 (124) 65.8 ( 768)
<NA> 1.2 ( 2) 1.7 ( 20)
neutrophils (mean (SD)) 133.447 (437.032) 158.140 (448.512) 0.754 80.9
Mast_cells_plaque (mean (SD)) 123.389 (135.924) 173.244 (168.601) 0.097 83.7
IPH.bin % (freq) no 39.1 ( 63) 36.4 ( 425) 0.521 1.5
yes 60.2 ( 97) 62.0 ( 723)
<NA> 0.6 ( 1) 1.6 ( 19)
vessel_density_averaged (mean (SD)) 8.837 (6.727) 8.438 (6.388) 0.478 8.0
[ reached getOption("max.print") -- omitted 22 rows ]CEA patients with plasma MCP1 levels
Showing the baseline table of the CEA patients in the Athero-Express Biobank with plasma MCP1 levels.
NOT AVAILABLE YET
AEDB.CEA.subset.plasma <- subset(AEDB.CEA, !is.na(MCP1_plasma))
AEDB.CEA.subset.plasma.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
strata = "AsymptSympt2G",
data = AEDB.CEA.subset.plasma, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:6]CEA patients with plasma and plaque MCP1 levels
Showing the baseline table of the CEA patients in the Athero-Express Biobank with both plasma and plaque MCP1 levels.
NOT AVAILABLE YET
AEDB.CEA.subset.both <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) & !is.na(MCP1))
AEDB.CEA.subset.both.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
strata = "AsymptSympt2G",
data = AEDB.CEA.subset.both, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:6]Writing the baseline table to Excel format.
# Write basetable
require(openxlsx)
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.xlsx"),
AEDB.CEA.tableOne,
row.names = TRUE,
col.names = TRUE,
sheetName = "wholeAEDB_Baseline")
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.AsymptSympt.xlsx"),
AEDB.CEA.subset.AsymptSympt.tableOne,
row.names = TRUE,
col.names = TRUE,
sheetName = "wholeAEDB_Baseline_Sympt")
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEA.xlsx"),
AEDB.CEA.subset.combo.tableOne,
row.names = TRUE,
col.names = TRUE,
sheetName = "subsetAEDB_Baseline")
# write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEAplasma.AsymptSympt.xlsx"),
# AEDB.CEA.subset.plasma.tableOne,
# row.names = TRUE,
# col.names = TRUE,
# sheetName = "subsetAEDB_Baseline_plasma_Sympt")
Data exploration
Here we inspect the data and when necessary transform quantitative measures. We will inspect the raw, natural log transformed + the smallest measurement, and inverse-normal transformation.
MCP1 plaque levels: experiment 2
We will explore the plaque levels. As noted above, we will use MCP1_pg_ug_2015, this was experiment 2 in 2015 on the LUMINEX-platform and measurements were corrected for total plaque protein content.
summary(AEDB.CEA$MCP1_pg_ug_2015) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0005 0.1374 0.3407 0.6123 0.7235 10.8540 1225 do.call(rbind , by(AEDB.CEA$MCP1_pg_ug_2015, AEDB.CEA$AsymptSympt2G, summary)) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
Asymptomatic 0.0061401090 0.09779678 0.2148984 0.4950529 0.4982360 5.761795 139
Symptomatic 0.0004584575 0.14499491 0.3510850 0.6266503 0.7427464 10.853968 1086summary(AEDB.CEA$MCP1_pg_ml_2015) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.66 101.34 298.76 600.44 770.98 10181.08 1224 do.call(rbind , by(AEDB.CEA$MCP1_pg_ml_2015, AEDB.CEA$AsymptSympt2G, summary)) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
Asymptomatic 9.36 71.1650 152.220 405.1822 537.9100 2669.59 139
Symptomatic 0.66 114.9425 314.625 624.3948 792.4225 10181.08 1085library(patchwork)
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.min_MCP1_pg_ug_2015 <- min(AEDB.CEA$MCP1_pg_ug_2015, na.rm = TRUE)
min_MCP1_pg_ug_2015[1] 0.0004584575AEDB.CEA$MCP1_pg_ug_2015_LN <- log(AEDB.CEA$MCP1_pg_ug_2015 + min_MCP1_pg_ug_2015)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
# title = "MCP1 plaque levels",
xlab = "natural log-transformed pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.AEDB.CEA$MCP1_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ug_2015)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "inverse-normal transformation pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "") | ggpar(p3, legend = "right")
rm(p1, p2, p3)We will explore the MCP1_pg_ml_2015 levels and compare to the protein content corrected ones.
library(patchwork)
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ml_2015",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "pg/mL",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.min_MCP1_pg_ml_2015 <- min(AEDB.CEA$MCP1_pg_ml_2015, na.rm = TRUE)
min_MCP1_pg_ml_2015[1] 0.66AEDB.CEA$MCP1_pg_ml_2015_LN <- log(AEDB.CEA$MCP1_pg_ml_2015 + min_MCP1_pg_ml_2015)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ml_2015_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
# title = "MCP1 plaque levels",
xlab = "natural log-transformed pg/mL",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.AEDB.CEA$MCP1_pg_ml_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ml_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ml_2015)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ml_2015_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "inverse-normal transformation pg/mL",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "") | ggpar(p3, legend = "right")
rm(p1, p2, p3)MCP1 plaque levels: experiment 1
We will explore the plaque levels. As noted above, we will use MCP1, this was experiment 1 on the LUMINEX-platform and measurements were corrected for total plaque protein content.
# summary(AEDB.CEA$MCP1)
#
# do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary))
#
# attach(AEDB.CEA)
AEDB.CEA$MCP1[MCP1 == 0] <- NA
# detach(AEDB.CEA)
summary(AEDB.CEA$MCP1) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
3.865 58.057 103.811 137.960 180.297 926.273 1867 do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary)) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
Asymptomatic 15.578813 45.31926 77.84731 119.4878 126.1851 846.5306 184
Symptomatic 3.864774 60.54905 111.87004 141.3406 186.4375 926.2729 1683p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "pg/mL",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.min_MCP1 <- min(AEDB.CEA$MCP1, na.rm = TRUE)
min_MCP1[1] 3.864774AEDB.CEA$MCP1_LN <- log(AEDB.CEA$MCP1 + min_MCP1)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "natural log-transformed pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "inverse-normal transformation pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "") | ggpar(p3, legend = "right")
rm(p1, p2, p3)
Correlations between MCP1 plaque levels and transformations
Here we compare the MCP1 plaque levels from experiment 1 with those experiment 2. The latter we measured in pg/mL and also corrected for the total protein content (pg/ug).
p1 <- ggpubr::ggscatter(AEDB.CEA,
x = "MCP1_rank",
y = "MCP1_pg_ml_2015_rank",
color = "#1290D9",
# fill = "Gender",
# palette = c("#1290D9", "#DB003F"),
add = "reg.line",
add.params = list(color = "black", linetype = 2),
cor.coef = TRUE, cor.method = "spearman",
xlab = "experiment 1",
ylab = "experiment 2",
title = "MCP1 plaque levels, INT, [pg/mL]",
ggtheme = theme_minimal())
p1 
p2 <- ggpubr::ggscatter(AEDB.CEA,
x = "MCP1_rank",
y = "MCP1_pg_ug_2015_rank",
color = "#1290D9",
# fill = "Gender",
# palette = c("#1290D9", "#DB003F"),
add = "reg.line",
add.params = list(color = "black", linetype = 2),
cor.coef = TRUE, cor.method = "spearman",
xlab = "experiment 1",
ylab = "experiment 2",
title = "MCP1 plaque levels, INT, [pg/mL]/[pg/ug]",
ggtheme = theme_minimal())
p2 
p3 <- ggpubr::ggscatter(AEDB.CEA,
x = "MCP1_pg_ml_2015_rank",
y = "MCP1_pg_ug_2015_rank",
color = "#1290D9",
# fill = "Gender",
# palette = c("#1290D9", "#DB003F"),
add = "reg.line",
add.params = list(color = "black", linetype = 2),
cor.coef = TRUE, cor.method = "spearman",
xlab = "experiment 2, [pg/mL]",
ylab = "experiment 2, [pg/ug]",
title = "MCP1 plaque levels, INT",
ggtheme = theme_minimal())
p3
MCP1 plasma levels
NOT AVAILABLE YET
Preliminary conclusion data exploration
In line with the previous work by Marios Georgakis we will apply natural log transformation on all proteins and focus the analysis on MCP1 in plasma and plaque.
Analyses
The analyses are focused on three elements:
- plaque vulnerability phenotypes
- clinical status at inclusion (symptoms)
- secondary clinical outcome during three (3) years of follow-up
Covariates & other variables
- Age (continuous in 1-year increment). [
Age] - Sex (male vs. female). [
Gender] - Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [
Hypertension.composite] - Presence of diabetes mellitus at baseline (defined either as a history of diabetes and/or administration of glucose lowering medication). [
DiabetesStatus] - Smoking (current, ex-, never). [
SmokerStatus] - LDL-C levels (continuous). [
LDL_final] - Use of lipid-lowering drugs. [
Med.Statin.LLD] - Use of antiplatelet drugs. [
Med.all.antiplatelet] - eGFR (continuous). [
GFR_MDRD] - BMI (continuous). [
BMI] - History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [
MedHx_CVD] combination of [CAD_history,Stroke_history,Peripheral.interv] - Level of stenosis (50-70% vs. 70-99%). [
stenose] - Year of surgery [
ORdate_year] as we discovered in Van Lammeren et al. the composition of the plaque and therefore the Athero-Express Biobank Study has changed over the years. Likely through changes in lifestyle and primary prevention regimes.
Models
We will analyze the data through four different models
- Model 1: adjusted for age, sex, and year of surgery
- Model 2: adjusted for age, sex, year of surgery, and additionally adjusted for history hypertension (defined from medical history and/or use of antihypertensive medications), diabetes (defined as history of a diagnosis and/or use of glucose-lowering medications), current smoking, LDL-C levels at time of operation, use of statins, use of antiplatelet agents, eGFR, BMI, history of cardiovascular disease (coronary artery disease, stroke, peripheral artery disease), and level of stenosis (50-70%, 70-90%, 90-99%)
A. Cross-sectional analysis plaque phenotypes
In the cross-sectional analysis of plaque and plasma MCP1, IL6, and IL6R levels we will focus on the following plaque vulnerability phenotypes:
- Percentage of macrophages (continuous trait)
- Percentage of SMCs (continuous trait)
- Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
- Presence of moderate/heavy calcifications (binary trait)
- Presence of moderate/heavy collagen content (binary trait)
- Presence of lipid core no/<10% vs. >10% (binary trait)
- Presence of intraplaque hemorrhage (binary trait)
Continous traits
# macrophages
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$macmean0) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.0733 0.3133 0.7671 0.9967 15.1000 720 min_macmean <- min(AEDB.CEA$macmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))
Minimum value % macrophages: 0.AEDB.CEA$Macrophages_LN <- log(AEDB.CEA$macmean0 + min_macmean)
ggpubr::gghistogram(AEDB.CEA, "Macrophages_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% macrophages",
xlab = "natural log-transformed %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$Macrophages_rank <- qnorm((rank(AEDB.CEA$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$macmean0)))
ggpubr::gghistogram(AEDB.CEA, "Macrophages_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% macrophages",
xlab = "inverse-rank normalized %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
# smooth muscle cells
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$macmean0) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.0733 0.3133 0.7671 0.9967 15.1000 720 min_smcmean <- min(AEDB.CEA$smcmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))
Minimum value % smooth muscle cells: 0.AEDB.CEA$SMC_LN <- log(AEDB.CEA$smcmean0 + min_smcmean)
ggpubr::gghistogram(AEDB.CEA, "SMC_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% smooth muscle cells",
xlab = "natural log-transformed %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$SMC_rank <- qnorm((rank(AEDB.CEA$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$smcmean0)))
ggpubr::gghistogram(AEDB.CEA, "SMC_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% smooth muscle cells",
xlab = "inverse-rank normalized %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
# vessel density
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$vessel_density_averaged) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 4.000 7.000 8.318 11.300 48.000 850 min_vesseldensity <- min(AEDB.CEA$vessel_density_averaged, na.rm = TRUE)
min_vesseldensity[1] 0cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))
Minimum value number of intraplaque neovessels per 3-4 hotspots: 0.AEDB.CEA$VesselDensity_LN <- log(AEDB.CEA$vessel_density_averaged + min_vesseldensity)
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "number of intraplaque neovessels per 3-4 hotspots",
xlab = "natural log-transformed number",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$VesselDensity_rank <- qnorm((rank(AEDB.CEA$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$vessel_density_averaged)))
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "number of intraplaque neovessels per 3-4 hotspots",
xlab = "inverse-rank normalized number",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
Binary traits
# calcification
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$Calc.bin) no/minor moderate/heavy NA's
1007 850 566 contrasts(AEDB.CEA$Calc.bin) moderate/heavy
no/minor 0
moderate/heavy 1AEDB.CEA$CalcificationPlaque <- as.factor(AEDB.CEA$Calc.bin)
df <- AEDB.CEA %>%
filter(!is.na(CalcificationPlaque)) %>%
group_by(Gender, CalcificationPlaque) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Calcification",
xlab = "calcification",
ggtheme = theme_minimal())
rm(df)
# collagen
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$Collagen.bin) no/minor moderate/heavy NA's
382 1469 572 contrasts(AEDB.CEA$Collagen.bin) moderate/heavy
no/minor 0
moderate/heavy 1AEDB.CEA$CollagenPlaque <- as.factor(AEDB.CEA$Collagen.bin)
df <- AEDB.CEA %>%
filter(!is.na(CollagenPlaque)) %>%
group_by(Gender, CollagenPlaque) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Collagen",
xlab = "collagen",
ggtheme = theme_minimal())
rm(df)
# fat 10%
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$Fat.bin_10) <10% >10% NA's
542 1316 565 contrasts(AEDB.CEA$Fat.bin_10) >10%
<10% 0
>10% 1AEDB.CEA$Fat10Perc <- as.factor(AEDB.CEA$Fat.bin_10)
df <- AEDB.CEA %>%
filter(!is.na(Fat10Perc)) %>%
group_by(Gender, Fat10Perc) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Intraplaque fat",
xlab = "intraplaque fat",
ggtheme = theme_minimal())
rm(df)
# macrophages binned
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$Macrophages.bin) no/minor moderate/heavy NA's
847 992 584 contrasts(AEDB.CEA$Macrophages.bin) moderate/heavy
no/minor 0
moderate/heavy 1AEDB.CEA$MAC_binned <- as.factor(AEDB.CEA$Macrophages.bin)
df <- AEDB.CEA %>%
filter(!is.na(MAC_binned)) %>%
group_by(Gender, MAC_binned) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "MAC_binned", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Macrophages (binned)",
xlab = "Macrophages",
ggtheme = theme_minimal())
rm(df)
# macrophages grouped
cat("Summary of data.\n")Summary of data.AEDB.CEA$macrophages <- as.factor(AEDB.CEA$macrophages)
summary(AEDB.CEA$macrophages)-888 0 1 2 3 NA's
6 173 674 786 206 578 contrasts(AEDB.CEA$macrophages) 0 1 2 3
-888 0 0 0 0
0 1 0 0 0
1 0 1 0 0
2 0 0 1 0
3 0 0 0 1AEDB.CEA$MAC_grouped <- as.factor(AEDB.CEA$macrophages)
df <- AEDB.CEA %>%
filter(!is.na(MAC_grouped)) %>%
group_by(Gender, MAC_grouped) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "MAC_grouped", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Macrophages (grouped)",
xlab = "Macrophages",
ggtheme = theme_minimal())
rm(df)
# SMC binned
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$SMC.bin) no/minor moderate/heavy NA's
602 1244 577 contrasts(AEDB.CEA$SMC.bin) moderate/heavy
no/minor 0
moderate/heavy 1AEDB.CEA$SMC_binned <- as.factor(AEDB.CEA$SMC.bin)
df <- AEDB.CEA %>%
filter(!is.na(SMC_binned)) %>%
group_by(Gender, SMC_binned) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "SMC_binned", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "SMC (binned)",
xlab = "SMC",
ggtheme = theme_minimal())
rm(df)
# SMC grouped
cat("Summary of data.\n")Summary of data.AEDB.CEA$smc <- as.factor(AEDB.CEA$smc)
summary(AEDB.CEA$smc)-888 0 1 2 3 NA's
4 44 558 908 336 573 contrasts(AEDB.CEA$smc) 0 1 2 3
-888 0 0 0 0
0 1 0 0 0
1 0 1 0 0
2 0 0 1 0
3 0 0 0 1AEDB.CEA$SMC_grouped <- as.factor(AEDB.CEA$smc)
df <- AEDB.CEA %>%
filter(!is.na(SMC_grouped)) %>%
group_by(Gender, SMC_grouped) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "SMC_grouped", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "SMC (grouped)",
xlab = "SMC",
ggtheme = theme_minimal())
rm(df)
# IPH
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$IPH.bin) no yes NA's
746 1108 569 contrasts(AEDB.CEA$IPH.bin) yes
no 0
yes 1AEDB.CEA$IPH <- as.factor(AEDB.CEA$IPH.bin)
df <- AEDB.CEA %>%
filter(!is.na(IPH)) %>%
group_by(Gender, IPH) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "IPH", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Intraplaque hemorrhage",
xlab = "intraplaque hemorrhage",
ggtheme = theme_minimal())
rm(df)
# Symptoms
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$AsymptSympt) Asymptomatic Ocular and others Symptomatic
270 541 1612 contrasts(AEDB.CEA$AsymptSympt) Ocular and others Symptomatic
Asymptomatic 0 0
Ocular and others 1 0
Symptomatic 0 1AEDB.CEA$AsymptSympt <- as.factor(AEDB.CEA$AsymptSympt)
df <- AEDB.CEA %>%
filter(!is.na(AsymptSympt)) %>%
group_by(Gender, AsymptSympt) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Symptoms",
xlab = "symptoms",
ggtheme = theme_minimal())
rm(df)
Correlations between MCP1 plaque levels and surgery year
Here we compare the MCP1 plaque levels from experiment 1 with those experiment 2. The latter we measured in pg/mL and also corrected for the total protein content (pg/ug).
p1 <- ggpubr::ggscatter(AEDB.CEA,
x = "ORyear",
y = "MCP1_rank",
color = "#1290D9",
# fill = "Gender",
# palette = c("#1290D9", "#DB003F"),
add = "reg.line",
add.params = list(color = "black", linetype = 2),
cor.coef = TRUE, cor.method = "spearman",
xlab = "year of surgery",
ylab = "experiment 1",
title = "MCP1 plaque levels, INT, [pg/mL]",
ggtheme = theme_minimal())
p1 
p2 <- ggpubr::ggscatter(AEDB.CEA,
x = "ORyear",
y = "MCP1_pg_ml_2015_rank",
color = "#1290D9",
# fill = "Gender",
# palette = c("#1290D9", "#DB003F"),
add = "reg.line",
add.params = list(color = "black", linetype = 2),
cor.coef = TRUE, cor.method = "spearman",
xlab = "year of surgery",
ylab = "experiment 2, [pg/mL]",
title = "MCP1 plaque levels, INT, [pg/mL]/[pg/ug]",
ggtheme = theme_minimal())
p2 
p3 <- ggpubr::ggscatter(AEDB.CEA,
x = "ORyear",
y = "MCP1_pg_ug_2015_rank",
color = "#1290D9",
# fill = "Gender",
# palette = c("#1290D9", "#DB003F"),
add = "reg.line",
add.params = list(color = "black", linetype = 2),
cor.coef = TRUE, cor.method = "spearman",
xlab = "year of surgery",
ylab = "experiment 2, [pg/ug]",
title = "MCP1 plaque levels, INT",
ggtheme = theme_minimal())
p3
In this section we make some variables to assist with analysis.
AEDB.CEA.samplesize = nrow(AEDB.CEA)
# TRAITS.PROTEIN = c("IL6_LN", "MCP1_LN", "IL6_pg_ug_2015_LN", "IL6R_pg_ug_2015_LN", "MCP1_pg_ug_2015_LN")
# TRAITS.PROTEIN.RANK = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank")
# TRAITS.PROTEIN.RANK = c("MCP1_pg_ug_2015_rank", "MCP1_rank")
TRAITS.PROTEIN.RANK = c("MCP1_pg_ug_2015_rank", "MCP1_pg_ml_2015_rank", "MCP1_rank")
# TRAITS.CON = c("Macrophages_LN", "SMC_LN", "VesselDensity_LN")
# TRAITS.CON.RANK = c("Macrophages_rank")
TRAITS.CON.RANK = c("Macrophages_rank", "SMC_rank", "VesselDensity_rank")
# TRAITS.BIN = c("MAC_binned")
TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH",
"MAC_binned", "SMC_binned")
# "Hospital",
# "Age", "Gender",
# "TC_final", "LDL_final", "HDL_final", "TG_final",
# "systolic", "diastoli", "GFR_MDRD", "BMI",
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite",
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin",
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015",
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"
# 1. Age (continuous in 1-year increment). [Age]
# 2. Sex (male vs. female). [Gender]
# 3. Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4. Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5. Smoking (current, ex-, never). [SmokerCurrent]
# 6. LDL-C levels (continuous). [LDL_final]
# 7. Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8. Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9. eGFR (continuous). [GFR_MDRD]
# 10. BMI (continuous). [BMI]
# 11. History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combinatino of: [CAD_history, Stroke_history, Peripheral.interv]
# 12. Level of stenosis (50-70% vs. 70-99%). [stenose]
# Models
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,
AEDB.CEA$ORdate_epoch <- as.numeric(AEDB.CEA$dateok)
AEDB.CEA$ORdate_year <- as_factor(year(AEDB.CEA$dateok))
COVARIATES_M1 = c("Age", "Gender", "ORdate_year")
# COVARIATES_M1 = c("Age", "Gender", "ORdate_epoch")
COVARIATES_M2 = c(COVARIATES_M1,
"Hypertension.composite", "DiabetesStatus",
"SmokerStatus",
# "SmokerCurrent",
"Med.Statin.LLD", "Med.all.antiplatelet",
"GFR_MDRD", "BMI",
# "CAD_history", "Stroke_history", "Peripheral.interv",
"MedHx_CVD",
"stenose")
# COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")
# COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")
# COVARIATES_M5 = c(COVARIATES_M2, "IL6_pg_ug_2015_LN")
# COVARIATES_M5rank = c(COVARIATES_M2, "IL6_pg_ug_2015_rank")
Model 1
In this model we correct for Age, Gender, and year of surgery.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Quantitative plaque traits
Analysis of continuous/quantitative plaque traits as a function of plasma/plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.CON.RANK)) {
TRAIT = TRAITS.CON.RANK[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ug_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 ORdate_year2007 ORdate_year2008 ORdate_year2009
0.33698 0.08254 -0.23351 -0.86647 -1.09367 -0.73236 -0.40319 -0.13374 -0.10481
ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013
0.26594 0.14151 0.09176 0.11712
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2354 -0.5940 -0.0176 0.5715 3.0820
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.395529 0.236042 1.676 0.09407 .
currentDF[, TRAIT] 0.077440 0.030022 2.579 0.01002 *
Age -0.001656 0.002937 -0.564 0.57288
Gendermale 0.081048 0.057998 1.397 0.16255
ORdate_year2003 -0.237655 0.150189 -1.582 0.11384
ORdate_year2004 -0.870719 0.143786 -6.056 1.89e-09 ***
ORdate_year2005 -1.092593 0.145227 -7.523 1.07e-13 ***
ORdate_year2006 -0.726541 0.147150 -4.937 9.08e-07 ***
ORdate_year2007 -0.404093 0.146442 -2.759 0.00588 **
ORdate_year2008 -0.133619 0.155734 -0.858 0.39107
ORdate_year2009 -0.105426 0.152871 -0.690 0.49056
ORdate_year2010 0.268019 0.158205 1.694 0.09051 .
ORdate_year2011 0.136285 0.153354 0.889 0.37435
ORdate_year2012 0.087239 0.157270 0.555 0.57920
ORdate_year2013 0.119901 0.539663 0.222 0.82422
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9103 on 1155 degrees of freedom
Multiple R-squared: 0.1817, Adjusted R-squared: 0.1717
F-statistic: 18.31 on 14 and 1155 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.07744
Standard error............: 0.030022
Odds ratio (effect size)..: 1.081
Lower 95% CI..............: 1.019
Upper 95% CI..............: 1.146
T-value...................: 2.579472
P-value...................: 0.01001788
R^2.......................: 0.181667
Adjusted r^2..............: 0.171747
Sample size of AE DB......: 2423
Sample size of model......: 1170
Missing data %............: 51.71275
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 ORdate_year2007 ORdate_year2008 ORdate_year2009
0.40642 -0.08209 -0.28892 -0.95737 -1.14560 -0.70123 -0.39969 -0.20811 -0.18243
ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013
0.10918 -0.01825 -0.05832 0.02947
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.10809 -0.59475 -0.01498 0.56375 3.13759
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.622913 0.243987 2.553 0.01081 *
currentDF[, TRAIT] -0.083746 0.030147 -2.778 0.00556 **
Age -0.003874 0.002978 -1.301 0.19357
Gendermale 0.068134 0.058531 1.164 0.24464
ORdate_year2003 -0.295016 0.152127 -1.939 0.05271 .
ORdate_year2004 -0.960309 0.147006 -6.532 9.69e-11 ***
ORdate_year2005 -1.142787 0.148465 -7.697 2.98e-14 ***
ORdate_year2006 -0.695525 0.147931 -4.702 2.89e-06 ***
ORdate_year2007 -0.399479 0.147518 -2.708 0.00687 **
ORdate_year2008 -0.202862 0.158167 -1.283 0.19990
ORdate_year2009 -0.179577 0.155721 -1.153 0.24907
ORdate_year2010 0.119282 0.161023 0.741 0.45898
ORdate_year2011 -0.013350 0.155386 -0.086 0.93155
ORdate_year2012 -0.056178 0.159299 -0.353 0.72441
ORdate_year2013 0.038548 0.540546 0.071 0.94316
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9106 on 1151 degrees of freedom
Multiple R-squared: 0.1817, Adjusted R-squared: 0.1717
F-statistic: 18.25 on 14 and 1151 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.083746
Standard error............: 0.030147
Odds ratio (effect size)..: 0.92
Lower 95% CI..............: 0.867
Upper 95% CI..............: 0.976
T-value...................: -2.777945
P-value...................: 0.005559273
R^2.......................: 0.181683
Adjusted r^2..............: 0.17173
Sample size of AE DB......: 2423
Sample size of model......: 1166
Missing data %............: 51.87784
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 ORdate_year2007 ORdate_year2008
0.320407 -0.043841 0.111624 -0.272605 -0.916934 -1.126262 -0.719577 -0.408076 -0.188508
ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013
-0.216319 0.153278 0.009214 -0.051196 0.020986
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1779 -0.6194 -0.0134 0.5554 3.1203
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.421304 0.245160 1.718 0.08599 .
currentDF[, TRAIT] -0.043792 0.029165 -1.502 0.13351
Age -0.001500 0.003074 -0.488 0.62570
Gendermale 0.111830 0.060542 1.847 0.06500 .
ORdate_year2003 -0.275200 0.154359 -1.783 0.07489 .
ORdate_year2004 -0.916664 0.147149 -6.229 6.70e-10 ***
ORdate_year2005 -1.123951 0.149419 -7.522 1.13e-13 ***
ORdate_year2006 -0.717789 0.150704 -4.763 2.17e-06 ***
ORdate_year2007 -0.406223 0.152930 -2.656 0.00802 **
ORdate_year2008 -0.185083 0.162822 -1.137 0.25591
ORdate_year2009 -0.213266 0.159375 -1.338 0.18113
ORdate_year2010 0.158011 0.163369 0.967 0.33366
ORdate_year2011 0.013455 0.157030 0.086 0.93174
ORdate_year2012 -0.048793 0.162960 -0.299 0.76468
ORdate_year2013 0.024963 0.546856 0.046 0.96360
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9212 on 1079 degrees of freedom
Multiple R-squared: 0.1794, Adjusted R-squared: 0.1687
F-statistic: 16.84 on 14 and 1079 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.043792
Standard error............: 0.029165
Odds ratio (effect size)..: 0.957
Lower 95% CI..............: 0.904
Upper 95% CI..............: 1.013
T-value...................: -1.501526
P-value...................: 0.1335122
R^2.......................: 0.179351
Adjusted r^2..............: 0.168703
Sample size of AE DB......: 2423
Sample size of model......: 1094
Missing data %............: 54.84936
Analysis of MCP1_pg_ml_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 ORdate_year2007 ORdate_year2008
-0.16288 0.08428 0.32878 -0.25413 -0.76643 -0.80078 -0.26303 -0.14007 0.29281
ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013
0.29073 0.43801 0.56392 0.45845 0.84232
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.84806 -0.54937 -0.01453 0.56092 3.00399
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.352959 0.224612 -1.571 0.116360
currentDF[, TRAIT] 0.086818 0.028558 3.040 0.002419 **
Age 0.002815 0.002795 1.007 0.314005
Gendermale 0.327756 0.055187 5.939 3.79e-09 ***
ORdate_year2003 -0.247841 0.142929 -1.734 0.083182 .
ORdate_year2004 -0.765754 0.136835 -5.596 2.73e-08 ***
ORdate_year2005 -0.804401 0.138207 -5.820 7.60e-09 ***
ORdate_year2006 -0.267054 0.140037 -1.907 0.056765 .
ORdate_year2007 -0.144154 0.139363 -1.034 0.301177
ORdate_year2008 0.287921 0.148206 1.943 0.052294 .
ORdate_year2009 0.287587 0.145481 1.977 0.048302 *
ORdate_year2010 0.432925 0.150557 2.875 0.004108 **
ORdate_year2011 0.558437 0.145939 3.826 0.000137 ***
ORdate_year2012 0.456015 0.149326 3.054 0.002311 **
ORdate_year2013 0.836326 0.513578 1.628 0.103706
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8663 on 1156 degrees of freedom
Multiple R-squared: 0.2475, Adjusted R-squared: 0.2384
F-statistic: 27.16 on 14 and 1156 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.086818
Standard error............: 0.028558
Odds ratio (effect size)..: 1.091
Lower 95% CI..............: 1.031
Upper 95% CI..............: 1.153
T-value...................: 3.040034
P-value...................: 0.002418718
R^2.......................: 0.247484
Adjusted r^2..............: 0.23837
Sample size of AE DB......: 2423
Sample size of model......: 1171
Missing data %............: 51.67148
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 ORdate_year2007 ORdate_year2008
-0.03097 -0.14724 0.29811 -0.33851 -0.91018 -0.90001 -0.23861 -0.14773 0.17349
ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013
0.16422 0.20639 0.33409 0.24594 0.70541
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.81766 -0.54558 -0.00956 0.54805 3.02418
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0195953 0.2299828 0.085 0.9321
currentDF[, TRAIT] -0.1484926 0.0284162 -5.226 2.06e-07 ***
Age -0.0007339 0.0028071 -0.261 0.7938
Gendermale 0.2979197 0.0551663 5.400 8.07e-08 ***
ORdate_year2003 -0.3404323 0.1434078 -2.374 0.0178 *
ORdate_year2004 -0.9111497 0.1385811 -6.575 7.37e-11 ***
ORdate_year2005 -0.9000922 0.1399559 -6.431 1.85e-10 ***
ORdate_year2006 -0.2380598 0.1394529 -1.707 0.0881 .
ORdate_year2007 -0.1472997 0.1390632 -1.059 0.2897
ORdate_year2008 0.1740095 0.1491021 1.167 0.2434
ORdate_year2009 0.1641912 0.1467960 1.118 0.2636
ORdate_year2010 0.2067945 0.1517939 1.362 0.1734
ORdate_year2011 0.3347231 0.1464800 2.285 0.0225 *
ORdate_year2012 0.2458210 0.1498537 1.640 0.1012
ORdate_year2013 0.7062072 0.5095673 1.386 0.1660
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8584 on 1152 degrees of freedom
Multiple R-squared: 0.2588, Adjusted R-squared: 0.2498
F-statistic: 28.74 on 14 and 1152 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.148493
Standard error............: 0.028416
Odds ratio (effect size)..: 0.862
Lower 95% CI..............: 0.815
Upper 95% CI..............: 0.911
T-value...................: -5.225639
P-value...................: 2.058918e-07
R^2.......................: 0.258833
Adjusted r^2..............: 0.249826
Sample size of AE DB......: 2423
Sample size of model......: 1167
Missing data %............: 51.83657
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 ORdate_year2007 ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011
-0.1223 0.3445 -0.2947 -0.8248 -0.8312 -0.2522 -0.1431 0.2516 0.1917 0.2774 0.4670
ORdate_year2012 ORdate_year2013
0.3379 0.7817
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.85581 -0.56391 -0.00244 0.56715 3.09490
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.290410 0.233765 -1.242 0.21439
currentDF[, TRAIT] -0.033503 0.027811 -1.205 0.22861
Age 0.002488 0.002931 0.849 0.39608
Gendermale 0.346303 0.057723 5.999 2.70e-09 ***
ORdate_year2003 -0.291375 0.147193 -1.980 0.04801 *
ORdate_year2004 -0.815017 0.140318 -5.808 8.29e-09 ***
ORdate_year2005 -0.826258 0.142483 -5.799 8.75e-09 ***
ORdate_year2006 -0.249346 0.143708 -1.735 0.08301 .
ORdate_year2007 -0.147747 0.145830 -1.013 0.31122
ORdate_year2008 0.248014 0.155263 1.597 0.11047
ORdate_year2009 0.186583 0.151977 1.228 0.21982
ORdate_year2010 0.268896 0.155785 1.726 0.08462 .
ORdate_year2011 0.444322 0.149740 2.967 0.00307 **
ORdate_year2012 0.313324 0.155027 2.021 0.04352 *
ORdate_year2013 0.752339 0.521470 1.443 0.14939
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8784 on 1080 degrees of freedom
Multiple R-squared: 0.2376, Adjusted R-squared: 0.2277
F-statistic: 24.04 on 14 and 1080 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.033503
Standard error............: 0.027811
Odds ratio (effect size)..: 0.967
Lower 95% CI..............: 0.916
Upper 95% CI..............: 1.021
T-value...................: -1.20464
P-value...................: 0.228606
R^2.......................: 0.237565
Adjusted r^2..............: 0.227681
Sample size of AE DB......: 2423
Sample size of model......: 1095
Missing data %............: 54.80809
Analysis of MCP1_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.2003 0.1176 0.2647 -0.1616 -0.5152 -0.5602 -0.9499
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4760 -0.6055 -0.0364 0.6435 2.9129
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.611198 0.343546 1.779 0.075781 .
currentDF[, TRAIT] 0.116279 0.038753 3.001 0.002818 **
Age -0.006139 0.004734 -1.297 0.195211
Gendermale 0.268083 0.090711 2.955 0.003258 **
ORdate_year2003 -0.169388 0.145331 -1.166 0.244313
ORdate_year2004 -0.514419 0.140123 -3.671 0.000265 ***
ORdate_year2005 -0.554838 0.139369 -3.981 7.79e-05 ***
ORdate_year2006 -0.935262 0.222533 -4.203 3.08e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9597 on 547 degrees of freedom
Multiple R-squared: 0.08984, Adjusted R-squared: 0.0782
F-statistic: 7.714 on 7 and 547 DF, p-value: 6.608e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.116279
Standard error............: 0.038753
Odds ratio (effect size)..: 1.123
Lower 95% CI..............: 1.041
Upper 95% CI..............: 1.212
T-value...................: 3.000536
P-value...................: 0.002817901
R^2.......................: 0.089844
Adjusted r^2..............: 0.078196
Sample size of AE DB......: 2423
Sample size of model......: 555
Missing data %............: 77.09451
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
1.37036 -0.25142 -0.01324 0.22548 -0.37756 -0.82687 -0.78270 -1.04179
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2033 -0.5595 -0.0592 0.6357 2.9156
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.370365 0.348243 3.935 9.39e-05 ***
currentDF[, TRAIT] -0.251423 0.040425 -6.219 9.95e-10 ***
Age -0.013244 0.004703 -2.816 0.00504 **
Gendermale 0.225484 0.088596 2.545 0.01120 *
ORdate_year2003 -0.377564 0.143749 -2.627 0.00887 **
ORdate_year2004 -0.826868 0.141179 -5.857 8.17e-09 ***
ORdate_year2005 -0.782698 0.140012 -5.590 3.59e-08 ***
ORdate_year2006 -1.041790 0.217292 -4.794 2.11e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9309 on 544 degrees of freedom
Multiple R-squared: 0.1382, Adjusted R-squared: 0.1271
F-statistic: 12.47 on 7 and 544 DF, p-value: 7.483e-15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.251423
Standard error............: 0.040425
Odds ratio (effect size)..: 0.778
Lower 95% CI..............: 0.718
Upper 95% CI..............: 0.842
T-value...................: -6.219464
P-value...................: 9.952175e-10
R^2.......................: 0.138235
Adjusted r^2..............: 0.127146
Sample size of AE DB......: 2423
Sample size of model......: 552
Missing data %............: 77.21832
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.2723 0.2989 -0.2369 -0.6282 -0.6192 -0.9273
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4402 -0.6106 -0.0202 0.6608 2.7828
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.724088 0.347451 2.084 0.03763 *
currentDF[, TRAIT] -0.046530 0.051026 -0.912 0.36224
Age -0.006816 0.004798 -1.421 0.15599
Gendermale 0.301681 0.092002 3.279 0.00111 **
ORdate_year2003 -0.240390 0.147816 -1.626 0.10448
ORdate_year2004 -0.608627 0.142405 -4.274 2.27e-05 ***
ORdate_year2005 -0.595420 0.143071 -4.162 3.68e-05 ***
ORdate_year2006 -0.901546 0.227905 -3.956 8.65e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9646 on 536 degrees of freedom
Multiple R-squared: 0.08305, Adjusted R-squared: 0.07108
F-statistic: 6.936 on 7 and 536 DF, p-value: 6.415e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.04653
Standard error............: 0.051026
Odds ratio (effect size)..: 0.955
Lower 95% CI..............: 0.864
Upper 95% CI..............: 1.055
T-value...................: -0.911886
P-value...................: 0.3622387
R^2.......................: 0.083055
Adjusted r^2..............: 0.07108
Sample size of AE DB......: 2423
Sample size of model......: 544
Missing data %............: 77.54849 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Binary plaque traits
Analysis of binary plaque traits as a function of plasma/plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.BIN)) {
TRAIT = TRAITS.BIN[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ug_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-1.15276 -0.25878 0.02156 -0.19700 0.07963 0.09044 0.33399 0.53682
ORdate_year2007 ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013 ORdate_year2014
-0.01096 -0.04191 -0.40867 -1.13732 -1.51676 -1.29592 -0.99827 -12.26332
Degrees of Freedom: 1179 Total (i.e. Null); 1164 Residual
Null Deviance: 1635
Residual Deviance: 1474 AIC: 1506
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9000 -1.1057 -0.5571 1.0390 2.1081
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.152757 0.548298 -2.102 0.035516 *
currentDF[, PROTEIN] -0.258780 0.068795 -3.762 0.000169 ***
Age 0.021557 0.006946 3.104 0.001912 **
Gendermale -0.196998 0.135903 -1.450 0.147183
ORdate_year2003 0.079632 0.334493 0.238 0.811829
ORdate_year2004 0.090436 0.326141 0.277 0.781557
ORdate_year2005 0.333989 0.337072 0.991 0.321757
ORdate_year2006 0.536816 0.338211 1.587 0.112462
ORdate_year2007 -0.010959 0.327021 -0.034 0.973268
ORdate_year2008 -0.041911 0.346823 -0.121 0.903816
ORdate_year2009 -0.408667 0.340571 -1.200 0.230160
ORdate_year2010 -1.137316 0.366915 -3.100 0.001937 **
ORdate_year2011 -1.516757 0.363330 -4.175 2.99e-05 ***
ORdate_year2012 -1.295922 0.366732 -3.534 0.000410 ***
ORdate_year2013 -0.998274 0.882699 -1.131 0.258083
ORdate_year2014 -12.263319 324.743820 -0.038 0.969877
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1635.3 on 1179 degrees of freedom
Residual deviance: 1473.7 on 1164 degrees of freedom
AIC: 1505.7
Number of Fisher Scoring iterations: 11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.25878
Standard error............: 0.068795
Odds ratio (effect size)..: 0.772
Lower 95% CI..............: 0.675
Upper 95% CI..............: 0.883
Z-value...................: -3.76162
P-value...................: 0.0001688163
Hosmer and Lemeshow r^2...: 0.098803
Cox and Snell r^2.........: 0.127962
Nagelkerke's pseudo r^2...: 0.170643
Sample size of AE DB......: 2423
Sample size of model......: 1180
Missing data %............: 51.30004
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
ORdate_year, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 ORdate_year2007 ORdate_year2008
1.36122 -0.14531 -0.58117 -0.08325 1.15946 0.82042 -0.58458 0.35273
ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013 ORdate_year2014
-0.44426 0.22838 -0.17137 -0.07051 -0.99901 -14.72841
Degrees of Freedom: 1180 Total (i.e. Null); 1167 Residual
Null Deviance: 1217
Residual Deviance: 1156 AIC: 1184
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3972 0.3573 0.6302 0.7526 1.1011
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.310e+00 6.421e-01 2.040 0.0413 *
currentDF[, PROTEIN] -1.456e-01 8.211e-02 -1.773 0.0763 .
Age 6.307e-04 8.055e-03 0.078 0.9376
Gendermale 1.284e-02 1.597e-01 0.080 0.9359
ORdate_year2003 -5.800e-01 3.869e-01 -1.499 0.1339
ORdate_year2004 -8.391e-02 3.938e-01 -0.213 0.8313
ORdate_year2005 1.158e+00 4.833e-01 2.397 0.0165 *
ORdate_year2006 8.200e-01 4.503e-01 1.821 0.0686 .
ORdate_year2007 -5.862e-01 3.807e-01 -1.540 0.1236
ORdate_year2008 3.512e-01 4.383e-01 0.801 0.4230
ORdate_year2009 -4.455e-01 3.950e-01 -1.128 0.2594
ORdate_year2010 2.272e-01 4.287e-01 0.530 0.5961
ORdate_year2011 -1.737e-01 3.954e-01 -0.439 0.6605
ORdate_year2012 -7.200e-02 4.105e-01 -0.175 0.8608
ORdate_year2013 -1.001e+00 8.317e-01 -1.203 0.2289
ORdate_year2014 -1.473e+01 5.354e+02 -0.028 0.9781
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1216.6 on 1180 degrees of freedom
Residual deviance: 1155.7 on 1165 degrees of freedom
AIC: 1187.7
Number of Fisher Scoring iterations: 12
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.145558
Standard error............: 0.082113
Odds ratio (effect size)..: 0.865
Lower 95% CI..............: 0.736
Upper 95% CI..............: 1.016
Z-value...................: -1.772653
P-value...................: 0.07628619
Hosmer and Lemeshow r^2...: 0.050031
Cox and Snell r^2.........: 0.050233
Nagelkerke's pseudo r^2...: 0.078118
Sample size of AE DB......: 2423
Sample size of model......: 1181
Missing data %............: 51.25877
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.86420 0.46340 0.01889 0.95051 0.77463 0.89766 0.89270 0.58665
ORdate_year2007 ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013 ORdate_year2014
0.11001 -0.63104 -0.60646 -0.39901 -1.12967 -1.08876 -0.40034 10.61751
Degrees of Freedom: 1180 Total (i.e. Null); 1165 Residual
Null Deviance: 1388
Residual Deviance: 1239 AIC: 1271
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6124 -0.9629 0.5625 0.8039 1.5940
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.864199 0.607049 -1.424 0.15456
currentDF[, PROTEIN] 0.463400 0.082063 5.647 1.63e-08 ***
Age 0.018893 0.007545 2.504 0.01228 *
Gendermale 0.950507 0.145500 6.533 6.46e-11 ***
ORdate_year2003 0.774629 0.423530 1.829 0.06740 .
ORdate_year2004 0.897656 0.402670 2.229 0.02580 *
ORdate_year2005 0.892701 0.411355 2.170 0.03000 *
ORdate_year2006 0.586651 0.402816 1.456 0.14529
ORdate_year2007 0.110010 0.391436 0.281 0.77868
ORdate_year2008 -0.631037 0.399013 -1.581 0.11376
ORdate_year2009 -0.606463 0.391994 -1.547 0.12183
ORdate_year2010 -0.399008 0.407400 -0.979 0.32738
ORdate_year2011 -1.129672 0.384436 -2.939 0.00330 **
ORdate_year2012 -1.088763 0.392820 -2.772 0.00558 **
ORdate_year2013 -0.400340 0.915726 -0.437 0.66198
ORdate_year2014 10.617507 324.743883 0.033 0.97392
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1387.7 on 1180 degrees of freedom
Residual deviance: 1239.4 on 1165 degrees of freedom
AIC: 1271.4
Number of Fisher Scoring iterations: 11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.4634
Standard error............: 0.082063
Odds ratio (effect size)..: 1.589
Lower 95% CI..............: 1.353
Upper 95% CI..............: 1.867
Z-value...................: 5.646868
P-value...................: 1.633976e-08
Hosmer and Lemeshow r^2...: 0.10692
Cox and Snell r^2.........: 0.118066
Nagelkerke's pseudo r^2...: 0.170812
Sample size of AE DB......: 2423
Sample size of model......: 1181
Missing data %............: 51.25877
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + ORdate_year,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 ORdate_year2007 ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011
0.76738 0.67016 -0.20924 -0.13741 -0.06569 -0.67660 -0.85264 -0.98192 -1.42311 -1.41333 -1.59431
ORdate_year2012 ORdate_year2013 ORdate_year2014
-1.13573 -0.31123 -14.00361
Degrees of Freedom: 1177 Total (i.e. Null); 1164 Residual
Null Deviance: 1576
Residual Deviance: 1472 AIC: 1500
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8721 -1.1589 0.6896 0.9595 1.6149
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.121646 0.565343 0.215 0.829633
currentDF[, PROTEIN] 0.052219 0.070235 0.743 0.457184
Age 0.009410 0.006845 1.375 0.169219
Gendermale 0.664410 0.134145 4.953 7.31e-07 ***
ORdate_year2003 -0.177363 0.387421 -0.458 0.647092
ORdate_year2004 -0.094676 0.378465 -0.250 0.802464
ORdate_year2005 -0.024357 0.387910 -0.063 0.949934
ORdate_year2006 -0.653339 0.374785 -1.743 0.081292 .
ORdate_year2007 -0.846454 0.370248 -2.286 0.022244 *
ORdate_year2008 -0.996677 0.387007 -2.575 0.010014 *
ORdate_year2009 -1.434044 0.380336 -3.770 0.000163 ***
ORdate_year2010 -1.450567 0.388429 -3.734 0.000188 ***
ORdate_year2011 -1.629340 0.376571 -4.327 1.51e-05 ***
ORdate_year2012 -1.159615 0.385039 -3.012 0.002598 **
ORdate_year2013 -0.341326 0.904718 -0.377 0.705970
ORdate_year2014 -14.035242 324.743866 -0.043 0.965527
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1576.1 on 1177 degrees of freedom
Residual deviance: 1469.2 on 1162 degrees of freedom
AIC: 1501.2
Number of Fisher Scoring iterations: 11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IPH
Effect size...............: 0.052219
Standard error............: 0.070235
Odds ratio (effect size)..: 1.054
Lower 95% CI..............: 0.918
Upper 95% CI..............: 1.209
Z-value...................: 0.743492
P-value...................: 0.4571839
Hosmer and Lemeshow r^2...: 0.067846
Cox and Snell r^2.........: 0.086776
Nagelkerke's pseudo r^2...: 0.117644
Sample size of AE DB......: 2423
Sample size of model......: 1178
Missing data %............: 51.38258
- processing MAC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 ORdate_year2007
-0.82062 0.27720 0.62424 0.14829 0.81291 1.28017 1.45942 1.04855
ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013
-0.17049 0.53234 0.07347 -0.33350 -1.05823 0.52723
Degrees of Freedom: 1174 Total (i.e. Null); 1161 Residual
Null Deviance: 1628
Residual Deviance: 1487 AIC: 1515
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7983 -1.0918 0.6769 1.0279 2.0074
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.231599 0.548722 -0.422 0.672973
currentDF[, PROTEIN] 0.275365 0.069669 3.952 7.73e-05 ***
Age -0.008748 0.006833 -1.280 0.200489
Gendermale 0.626746 0.135429 4.628 3.69e-06 ***
ORdate_year2003 0.132164 0.346010 0.382 0.702486
ORdate_year2004 0.812947 0.337912 2.406 0.016137 *
ORdate_year2005 1.291925 0.352112 3.669 0.000243 ***
ORdate_year2006 1.468127 0.354102 4.146 3.38e-05 ***
ORdate_year2007 1.059531 0.344161 3.079 0.002080 **
ORdate_year2008 -0.153722 0.360732 -0.426 0.670006
ORdate_year2009 0.545044 0.351293 1.552 0.120773
ORdate_year2010 0.096059 0.357595 0.269 0.788218
ORdate_year2011 -0.310076 0.347795 -0.892 0.372635
ORdate_year2012 -1.043765 0.381329 -2.737 0.006197 **
ORdate_year2013 0.545411 0.821188 0.664 0.506579
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1627.9 on 1174 degrees of freedom
Residual deviance: 1485.5 on 1160 degrees of freedom
AIC: 1515.5
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MAC_binned
Effect size...............: 0.275365
Standard error............: 0.069669
Odds ratio (effect size)..: 1.317
Lower 95% CI..............: 1.149
Upper 95% CI..............: 1.51
Z-value...................: 3.952494
P-value...................: 7.734095e-05
Hosmer and Lemeshow r^2...: 0.087478
Cox and Snell r^2.........: 0.114137
Nagelkerke's pseudo r^2...: 0.152227
Sample size of AE DB......: 2423
Sample size of model......: 1175
Missing data %............: 51.5064
- processing SMC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
3.01086 -0.15407 -0.02970 -0.37159 -0.11349 0.14267 0.30103 0.29080
ORdate_year2007 ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013 ORdate_year2014
0.06215 0.09156 0.37188 0.35192 0.11284 -0.85435 0.32768 -13.06366
Degrees of Freedom: 1175 Total (i.e. Null); 1160 Residual
Null Deviance: 1469
Residual Deviance: 1408 AIC: 1440
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9743 -1.3087 0.7395 0.8770 1.6720
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.010859 0.587320 5.126 2.95e-07 ***
currentDF[, PROTEIN] -0.154071 0.071727 -2.148 0.03171 *
Age -0.029705 0.007319 -4.059 4.93e-05 ***
Gendermale -0.371591 0.144156 -2.578 0.00995 **
ORdate_year2003 -0.113492 0.358067 -0.317 0.75128
ORdate_year2004 0.142670 0.353005 0.404 0.68610
ORdate_year2005 0.301033 0.364452 0.826 0.40881
ORdate_year2006 0.290798 0.362573 0.802 0.42253
ORdate_year2007 0.062151 0.351499 0.177 0.85965
ORdate_year2008 0.091558 0.371935 0.246 0.80555
ORdate_year2009 0.371885 0.373104 0.997 0.31889
ORdate_year2010 0.351922 0.379197 0.928 0.35337
ORdate_year2011 0.112839 0.357773 0.315 0.75246
ORdate_year2012 -0.854350 0.361900 -2.361 0.01824 *
ORdate_year2013 0.327683 0.890879 0.368 0.71301
ORdate_year2014 -13.063661 324.743838 -0.040 0.96791
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1469.3 on 1175 degrees of freedom
Residual deviance: 1408.4 on 1160 degrees of freedom
AIC: 1440.4
Number of Fisher Scoring iterations: 11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: SMC_binned
Effect size...............: -0.154071
Standard error............: 0.071727
Odds ratio (effect size)..: 0.857
Lower 95% CI..............: 0.745
Upper 95% CI..............: 0.987
Z-value...................: -2.148004
P-value...................: 0.0317134
Hosmer and Lemeshow r^2...: 0.041497
Cox and Snell r^2.........: 0.050527
Nagelkerke's pseudo r^2...: 0.070831
Sample size of AE DB......: 2423
Sample size of model......: 1176
Missing data %............: 51.46513
Analysis of MCP1_pg_ml_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + ORdate_year, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 ORdate_year2007
-1.44074 -0.32281 0.02287 0.05854 0.06294 0.35697 0.64638 0.04503
ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013 ORdate_year2014
0.08149 -0.29648 -1.08292 -1.39377 -1.20713 -0.92391 -12.29382
Degrees of Freedom: 1180 Total (i.e. Null); 1166 Residual
Null Deviance: 1637
Residual Deviance: 1471 AIC: 1501
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9023 -1.1008 -0.5294 1.0375 2.1708
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.362361 0.549989 -2.477 0.013247 *
currentDF[, PROTEIN] -0.312155 0.073630 -4.239 2.24e-05 ***
Age 0.022895 0.006965 3.287 0.001012 **
Gendermale -0.118872 0.137848 -0.862 0.388499
ORdate_year2003 0.060048 0.335047 0.179 0.857762
ORdate_year2004 0.074309 0.326042 0.228 0.819713
ORdate_year2005 0.367357 0.334888 1.097 0.272662
ORdate_year2006 0.645216 0.336156 1.919 0.054934 .
ORdate_year2007 0.052674 0.326837 0.161 0.871966
ORdate_year2008 0.080548 0.347949 0.231 0.816932
ORdate_year2009 -0.297681 0.341523 -0.872 0.383412
ORdate_year2010 -1.091316 0.368106 -2.965 0.003030 **
ORdate_year2011 -1.394006 0.365116 -3.818 0.000135 ***
ORdate_year2012 -1.207795 0.367754 -3.284 0.001023 **
ORdate_year2013 -0.920184 0.885011 -1.040 0.298459
ORdate_year2014 -12.265422 324.743819 -0.038 0.969871
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1636.6 on 1180 degrees of freedom
Residual deviance: 1470.2 on 1165 degrees of freedom
AIC: 1502.2
Number of Fisher Scoring iterations: 11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.312155
Standard error............: 0.07363
Odds ratio (effect size)..: 0.732
Lower 95% CI..............: 0.634
Upper 95% CI..............: 0.845
Z-value...................: -4.239494
P-value...................: 2.240244e-05
Hosmer and Lemeshow r^2...: 0.101675
Cox and Snell r^2.........: 0.131422
Nagelkerke's pseudo r^2...: 0.17526
Sample size of AE DB......: 2423
Sample size of model......: 1181
Missing data %............: 51.25877
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
ORdate_year, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 ORdate_year2007 ORdate_year2008
1.34657 -0.26315 -0.62930 -0.16869 1.11262 0.85594 -0.56479 0.44564
ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013 ORdate_year2014
-0.36405 0.29120 -0.05358 0.03872 -0.92356 -14.65333
Degrees of Freedom: 1181 Total (i.e. Null); 1168 Residual
Null Deviance: 1217
Residual Deviance: 1150 AIC: 1178
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4538 0.3467 0.6037 0.7479 1.1411
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.181034 0.643388 1.836 0.06641 .
currentDF[, PROTEIN] -0.273696 0.087082 -3.143 0.00167 **
Age 0.001495 0.008092 0.185 0.85345
Gendermale 0.098686 0.163018 0.605 0.54493
ORdate_year2003 -0.630061 0.389254 -1.619 0.10553
ORdate_year2004 -0.179856 0.394923 -0.455 0.64881
ORdate_year2005 1.102687 0.481381 2.291 0.02198 *
ORdate_year2006 0.854495 0.448409 1.906 0.05670 .
ORdate_year2007 -0.574407 0.380938 -1.508 0.13159
ORdate_year2008 0.443062 0.440195 1.007 0.31417
ORdate_year2009 -0.366704 0.396580 -0.925 0.35514
ORdate_year2010 0.293998 0.431294 0.682 0.49545
ORdate_year2011 -0.057820 0.398625 -0.145 0.88467
ORdate_year2012 0.036218 0.412844 0.088 0.93009
ORdate_year2013 -0.926518 0.838573 -1.105 0.26921
ORdate_year2014 -14.673249 535.411274 -0.027 0.97814
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1217.1 on 1181 degrees of freedom
Residual deviance: 1149.4 on 1166 degrees of freedom
AIC: 1181.4
Number of Fisher Scoring iterations: 12
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.273696
Standard error............: 0.087082
Odds ratio (effect size)..: 0.761
Lower 95% CI..............: 0.641
Upper 95% CI..............: 0.902
Z-value...................: -3.142972
P-value...................: 0.00167242
Hosmer and Lemeshow r^2...: 0.055626
Cox and Snell r^2.........: 0.055667
Nagelkerke's pseudo r^2...: 0.086591
Sample size of AE DB......: 2423
Sample size of model......: 1182
Missing data %............: 51.2175
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.489772 0.546702 0.016646 0.800078 0.794860 0.917833 0.830723 0.400608
ORdate_year2007 ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013 ORdate_year2014
0.001402 -0.839783 -0.796924 -0.474452 -1.356362 -1.308085 -0.525406 10.635042
Degrees of Freedom: 1181 Total (i.e. Null); 1166 Residual
Null Deviance: 1390
Residual Deviance: 1231 AIC: 1263
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.7194 -0.9325 0.5623 0.7853 1.7389
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.489772 0.610585 -0.802 0.422474
currentDF[, PROTEIN] 0.546702 0.084767 6.449 1.12e-10 ***
Age 0.016646 0.007568 2.199 0.027846 *
Gendermale 0.800078 0.147388 5.428 5.69e-08 ***
ORdate_year2003 0.794860 0.425139 1.870 0.061533 .
ORdate_year2004 0.917833 0.404062 2.272 0.023116 *
ORdate_year2005 0.830723 0.409483 2.029 0.042488 *
ORdate_year2006 0.400608 0.402257 0.996 0.319298
ORdate_year2007 0.001402 0.392662 0.004 0.997151
ORdate_year2008 -0.839783 0.402416 -2.087 0.036901 *
ORdate_year2009 -0.796924 0.396539 -2.010 0.044463 *
ORdate_year2010 -0.474452 0.410293 -1.156 0.247529
ORdate_year2011 -1.356362 0.390507 -3.473 0.000514 ***
ORdate_year2012 -1.308085 0.396862 -3.296 0.000980 ***
ORdate_year2013 -0.525406 0.926520 -0.567 0.570663
ORdate_year2014 10.635042 324.743879 0.033 0.973875
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1390.3 on 1181 degrees of freedom
Residual deviance: 1230.6 on 1166 degrees of freedom
AIC: 1262.6
Number of Fisher Scoring iterations: 11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.546702
Standard error............: 0.084767
Odds ratio (effect size)..: 1.728
Lower 95% CI..............: 1.463
Upper 95% CI..............: 2.04
Z-value...................: 6.449457
P-value...................: 1.122518e-10
Hosmer and Lemeshow r^2...: 0.11488
Cox and Snell r^2.........: 0.126396
Nagelkerke's pseudo r^2...: 0.182767
Sample size of AE DB......: 2423
Sample size of model......: 1182
Missing data %............: 51.2175
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 ORdate_year2007
0.82147 0.25748 0.58469 -0.13609 0.05389 0.13734 -0.63174 -0.83631
ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013 ORdate_year2014
-1.06506 -1.50988 -1.52679 -1.74460 -1.27965 -0.43284 -14.22594
Degrees of Freedom: 1178 Total (i.e. Null); 1164 Residual
Null Deviance: 1578
Residual Deviance: 1461 AIC: 1491
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9822 -1.1577 0.6924 0.9648 1.6660
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.237038 0.568222 0.417 0.676563
currentDF[, PROTEIN] 0.255430 0.074188 3.443 0.000575 ***
Age 0.008721 0.006875 1.269 0.204608
Gendermale 0.583473 0.136618 4.271 1.95e-05 ***
ORdate_year2003 -0.121390 0.390108 -0.311 0.755670
ORdate_year2004 0.049892 0.379298 0.132 0.895349
ORdate_year2005 0.119915 0.387309 0.310 0.756857
ORdate_year2006 -0.643402 0.374392 -1.719 0.085700 .
ORdate_year2007 -0.849404 0.371985 -2.283 0.022405 *
ORdate_year2008 -1.085574 0.390618 -2.779 0.005451 **
ORdate_year2009 -1.525635 0.384278 -3.970 7.18e-05 ***
ORdate_year2010 -1.550834 0.392692 -3.949 7.84e-05 ***
ORdate_year2011 -1.771175 0.382203 -4.634 3.58e-06 ***
ORdate_year2012 -1.298707 0.388844 -3.340 0.000838 ***
ORdate_year2013 -0.449779 0.906191 -0.496 0.619654
ORdate_year2014 -14.205147 324.743869 -0.044 0.965110
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1578.0 on 1178 degrees of freedom
Residual deviance: 1459.3 on 1163 degrees of freedom
AIC: 1491.3
Number of Fisher Scoring iterations: 11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IPH
Effect size...............: 0.25543
Standard error............: 0.074188
Odds ratio (effect size)..: 1.291
Lower 95% CI..............: 1.116
Upper 95% CI..............: 1.493
Z-value...................: 3.443003
P-value...................: 0.0005752928
Hosmer and Lemeshow r^2...: 0.075193
Cox and Snell r^2.........: 0.095739
Nagelkerke's pseudo r^2...: 0.129775
Sample size of AE DB......: 2423
Sample size of model......: 1179
Missing data %............: 51.34131
- processing MAC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.007281 0.359130 -0.010360 0.533974 0.161109 0.853809 1.286386 1.366591
ORdate_year2007 ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013
1.003752 -0.293263 0.418250 0.024109 -0.469331 -1.201501 0.448130
Degrees of Freedom: 1175 Total (i.e. Null); 1161 Residual
Null Deviance: 1629
Residual Deviance: 1477 AIC: 1507
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8498 -1.0958 0.6466 1.0116 2.0571
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.007281 0.549622 0.013 0.989430
currentDF[, PROTEIN] 0.359130 0.073601 4.879 1.06e-06 ***
Age -0.010360 0.006862 -1.510 0.131070
Gendermale 0.533974 0.137441 3.885 0.000102 ***
ORdate_year2003 0.161109 0.347117 0.464 0.642552
ORdate_year2004 0.853809 0.337919 2.527 0.011515 *
ORdate_year2005 1.286386 0.349366 3.682 0.000231 ***
ORdate_year2006 1.366591 0.351281 3.890 0.000100 ***
ORdate_year2007 1.003752 0.344047 2.917 0.003529 **
ORdate_year2008 -0.293263 0.362257 -0.810 0.418202
ORdate_year2009 0.418250 0.352741 1.186 0.235735
ORdate_year2010 0.024109 0.359993 0.067 0.946604
ORdate_year2011 -0.469331 0.351097 -1.337 0.181302
ORdate_year2012 -1.201501 0.384098 -3.128 0.001759 **
ORdate_year2013 0.448130 0.824123 0.544 0.586603
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1629.3 on 1175 degrees of freedom
Residual deviance: 1477.4 on 1161 degrees of freedom
AIC: 1507.4
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MAC_binned
Effect size...............: 0.35913
Standard error............: 0.073601
Odds ratio (effect size)..: 1.432
Lower 95% CI..............: 1.24
Upper 95% CI..............: 1.654
Z-value...................: 4.879394
P-value...................: 1.064125e-06
Hosmer and Lemeshow r^2...: 0.093216
Cox and Snell r^2.........: 0.121156
Nagelkerke's pseudo r^2...: 0.161586
Sample size of AE DB......: 2423
Sample size of model......: 1176
Missing data %............: 51.46513
- processing SMC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
2.88745 -0.29897 -0.02914 -0.28497 -0.15528 0.04587 0.23793 0.33696
ORdate_year2007 ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013 ORdate_year2014
0.08851 0.20027 0.48275 0.43851 0.25281 -0.72168 0.43697 -12.97963
Degrees of Freedom: 1176 Total (i.e. Null); 1161 Residual
Null Deviance: 1470
Residual Deviance: 1399 AIC: 1431
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0161 -1.2759 0.7239 0.8799 1.6956
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.887452 0.588885 4.903 9.43e-07 ***
currentDF[, PROTEIN] -0.298966 0.076712 -3.897 9.73e-05 ***
Age -0.029143 0.007357 -3.961 7.46e-05 ***
Gendermale -0.284969 0.146439 -1.946 0.0517 .
ORdate_year2003 -0.155276 0.360179 -0.431 0.6664
ORdate_year2004 0.045872 0.353852 0.130 0.8969
ORdate_year2005 0.237928 0.362557 0.656 0.5117
ORdate_year2006 0.336956 0.361237 0.933 0.3509
ORdate_year2007 0.088513 0.352134 0.251 0.8015
ORdate_year2008 0.200272 0.374404 0.535 0.5927
ORdate_year2009 0.482751 0.375790 1.285 0.1989
ORdate_year2010 0.438508 0.382365 1.147 0.2515
ORdate_year2011 0.252806 0.361521 0.699 0.4844
ORdate_year2012 -0.721679 0.363665 -1.984 0.0472 *
ORdate_year2013 0.436975 0.897699 0.487 0.6264
ORdate_year2014 -12.979630 324.743837 -0.040 0.9681
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1470.1 on 1176 degrees of freedom
Residual deviance: 1399.3 on 1161 degrees of freedom
AIC: 1431.3
Number of Fisher Scoring iterations: 11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: SMC_binned
Effect size...............: -0.298966
Standard error............: 0.076712
Odds ratio (effect size)..: 0.742
Lower 95% CI..............: 0.638
Upper 95% CI..............: 0.862
Z-value...................: -3.897236
P-value...................: 9.729663e-05
Hosmer and Lemeshow r^2...: 0.048143
Cox and Snell r^2.........: 0.058359
Nagelkerke's pseudo r^2...: 0.081826
Sample size of AE DB......: 2423
Sample size of model......: 1177
Missing data %............: 51.42386
Analysis of MCP1_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN],
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN]
0.3958 -0.1556
Degrees of Freedom: 555 Total (i.e. Null); 554 Residual
Null Deviance: 749.7
Residual Deviance: 746.5 AIC: 750.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7107 -1.2725 0.8729 1.0317 1.3256
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.61876 0.73578 -0.841 0.4004
currentDF[, PROTEIN] -0.09477 0.09111 -1.040 0.2982
Age 0.01180 0.01018 1.159 0.2466
Gendermale -0.15457 0.19762 -0.782 0.4341
ORdate_year2003 0.16108 0.30327 0.531 0.5953
ORdate_year2004 0.29545 0.29583 0.999 0.3179
ORdate_year2005 0.57055 0.29994 1.902 0.0571 .
ORdate_year2006 0.88014 0.51359 1.714 0.0866 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 749.67 on 555 degrees of freedom
Residual deviance: 738.03 on 548 degrees of freedom
AIC: 754.03
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.094774
Standard error............: 0.09111
Odds ratio (effect size)..: 0.91
Lower 95% CI..............: 0.761
Upper 95% CI..............: 1.087
Z-value...................: -1.040222
P-value...................: 0.2982367
Hosmer and Lemeshow r^2...: 0.015523
Cox and Snell r^2.........: 0.020712
Nagelkerke's pseudo r^2...: 0.027977
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + ORdate_year, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
2.9537 -0.5215 -0.0224 -0.4793 -0.1316 1.2289 0.9631
Degrees of Freedom: 553 Total (i.e. Null); 547 Residual
Null Deviance: 538
Residual Deviance: 482 AIC: 496
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4949 0.2914 0.4681 0.7127 1.3286
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.00203 0.99954 3.003 0.00267 **
currentDF[, PROTEIN] -0.51489 0.12280 -4.193 2.75e-05 ***
Age -0.02204 0.01373 -1.605 0.10856
Gendermale -0.10528 0.26559 -0.396 0.69182
ORdate_year2003 -0.47617 0.35632 -1.336 0.18143
ORdate_year2004 -0.12640 0.35921 -0.352 0.72492
ORdate_year2005 1.22929 0.43550 2.823 0.00476 **
ORdate_year2006 0.97027 0.80820 1.201 0.22994
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 537.98 on 553 degrees of freedom
Residual deviance: 481.84 on 546 degrees of freedom
AIC: 497.84
Number of Fisher Scoring iterations: 5
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.514891
Standard error............: 0.122802
Odds ratio (effect size)..: 0.598
Lower 95% CI..............: 0.47
Upper 95% CI..............: 0.76
Z-value...................: -4.192865
P-value...................: 2.754532e-05
Hosmer and Lemeshow r^2...: 0.104355
Cox and Snell r^2.........: 0.096372
Nagelkerke's pseudo r^2...: 0.155106
Sample size of AE DB......: 2423
Sample size of model......: 554
Missing data %............: 77.13578
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale
1.2197 0.6668 0.5508
Degrees of Freedom: 555 Total (i.e. Null); 553 Residual
Null Deviance: 538.8
Residual Deviance: 497.2 AIC: 503.2
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5032 0.3550 0.5157 0.6553 1.3571
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.441333 0.928439 0.475 0.6345
currentDF[, PROTEIN] 0.701197 0.124587 5.628 1.82e-08 ***
Age 0.004813 0.013029 0.369 0.7118
Gendermale 0.520801 0.237908 2.189 0.0286 *
ORdate_year2003 0.491261 0.397761 1.235 0.2168
ORdate_year2004 0.560708 0.377282 1.486 0.1372
ORdate_year2005 0.604522 0.380507 1.589 0.1121
ORdate_year2006 0.442659 0.586387 0.755 0.4503
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 538.82 on 555 degrees of freedom
Residual deviance: 494.33 on 548 degrees of freedom
AIC: 510.33
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.701197
Standard error............: 0.124587
Odds ratio (effect size)..: 2.016
Lower 95% CI..............: 1.579
Upper 95% CI..............: 2.574
Z-value...................: 5.628155
P-value...................: 1.821478e-08
Hosmer and Lemeshow r^2...: 0.082575
Cox and Snell r^2.........: 0.076905
Nagelkerke's pseudo r^2...: 0.123926
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale
-0.76646 0.02073 0.78990
Degrees of Freedom: 555 Total (i.e. Null); 553 Residual
Null Deviance: 611.8
Residual Deviance: 594.4 AIC: 600.4
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9870 0.5461 0.6436 0.7295 1.2034
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.66569 0.83518 -0.797 0.425410
currentDF[, PROTEIN] 0.06141 0.10472 0.586 0.557608
Age 0.02100 0.01164 1.804 0.071248 .
Gendermale 0.78794 0.21294 3.700 0.000215 ***
ORdate_year2003 -0.19354 0.36389 -0.532 0.594826
ORdate_year2004 -0.13588 0.35536 -0.382 0.702176
ORdate_year2005 -0.03569 0.35873 -0.099 0.920744
ORdate_year2006 -0.37398 0.54912 -0.681 0.495842
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 611.78 on 555 degrees of freedom
Residual deviance: 593.20 on 548 degrees of freedom
AIC: 609.2
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IPH
Effect size...............: 0.06141
Standard error............: 0.104725
Odds ratio (effect size)..: 1.063
Lower 95% CI..............: 0.866
Upper 95% CI..............: 1.306
Z-value...................: 0.586399
P-value...................: 0.5576077
Hosmer and Lemeshow r^2...: 0.030383
Cox and Snell r^2.........: 0.032878
Nagelkerke's pseudo r^2...: 0.049275
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing MAC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.5059 0.3877 0.3286 0.2050 0.6318 1.0095 2.1690
Degrees of Freedom: 551 Total (i.e. Null); 545 Residual
Null Deviance: 749.1
Residual Deviance: 708.9 AIC: 722.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1010 -1.1975 0.7712 1.0351 1.5450
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.43956 0.75843 0.580 0.562212
currentDF[, PROTEIN] 0.38092 0.09586 3.973 7.08e-05 ***
Age -0.01413 0.01047 -1.350 0.177052
Gendermale 0.33873 0.19929 1.700 0.089181 .
ORdate_year2003 0.18753 0.31633 0.593 0.553307
ORdate_year2004 0.63249 0.31044 2.037 0.041607 *
ORdate_year2005 1.02147 0.31570 3.236 0.001214 **
ORdate_year2006 2.19183 0.61695 3.553 0.000381 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 749.15 on 551 degrees of freedom
Residual deviance: 707.06 on 544 degrees of freedom
AIC: 723.06
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MAC_binned
Effect size...............: 0.380917
Standard error............: 0.095865
Odds ratio (effect size)..: 1.464
Lower 95% CI..............: 1.213
Upper 95% CI..............: 1.766
Z-value...................: 3.97348
P-value...................: 7.083006e-05
Hosmer and Lemeshow r^2...: 0.056181
Cox and Snell r^2.........: 0.073412
Nagelkerke's pseudo r^2...: 0.098856
Sample size of AE DB......: 2423
Sample size of model......: 552
Missing data %............: 77.21832
- processing SMC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
3.88576 -0.48364 -0.03704 -0.58286
Degrees of Freedom: 552 Total (i.e. Null); 549 Residual
Null Deviance: 667.1
Residual Deviance: 626 AIC: 634
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1783 -1.2106 0.6522 0.8369 1.4797
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.92710 0.87828 4.471 7.77e-06 ***
currentDF[, PROTEIN] -0.44223 0.10574 -4.182 2.89e-05 ***
Age -0.03968 0.01189 -3.336 0.000849 ***
Gendermale -0.59332 0.23415 -2.534 0.011280 *
ORdate_year2003 -0.12565 0.33463 -0.375 0.707293
ORdate_year2004 0.15887 0.33132 0.480 0.631575
ORdate_year2005 0.33894 0.33593 1.009 0.312998
ORdate_year2006 0.65902 0.58890 1.119 0.263111
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 667.10 on 552 degrees of freedom
Residual deviance: 621.78 on 545 degrees of freedom
AIC: 637.78
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: SMC_binned
Effect size...............: -0.442231
Standard error............: 0.105738
Odds ratio (effect size)..: 0.643
Lower 95% CI..............: 0.522
Upper 95% CI..............: 0.791
Z-value...................: -4.182321
P-value...................: 2.885487e-05
Hosmer and Lemeshow r^2...: 0.067942
Cox and Snell r^2.........: 0.078692
Nagelkerke's pseudo r^2...: 0.112303
Sample size of AE DB......: 2423
Sample size of model......: 553
Missing data %............: 77.17705 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Model 2
In this model we correct for Age, Gender, year of surgery, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Quantitative plaque traits
Analysis of continuous/quantitative plaque traits as a function of plasma/plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.CON.RANK)) {
TRAIT = TRAITS.CON.RANK[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year +
Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose,
data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ug_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Hypertension.composite + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 ORdate_year2007
0.571302 0.078133 -0.118396 -0.771998 -0.976042 -0.659333 -0.280396
ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013 Hypertension.compositeyes
-0.027688 0.001793 0.360498 0.281881 0.195132 0.255570 -0.195092
Med.Statin.LLDyes
-0.178177
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1576 -0.6055 -0.0077 0.5480 3.1718
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.8605008 0.5662381 1.520 0.12891
currentDF[, TRAIT] 0.0714488 0.0322866 2.213 0.02713 *
Age -0.0047941 0.0036193 -1.325 0.18561
Gendermale 0.0892693 0.0644981 1.384 0.16665
ORdate_year2003 -0.1402507 0.1573634 -0.891 0.37301
ORdate_year2004 -0.7832333 0.1505252 -5.203 2.38e-07 ***
ORdate_year2005 -0.9877556 0.1535482 -6.433 1.95e-10 ***
ORdate_year2006 -0.6684926 0.1563144 -4.277 2.08e-05 ***
ORdate_year2007 -0.2956370 0.1591219 -1.858 0.06348 .
ORdate_year2008 -0.0430340 0.1686538 -0.255 0.79865
ORdate_year2009 -0.0020761 0.1626983 -0.013 0.98982
ORdate_year2010 0.3600827 0.1654955 2.176 0.02981 *
ORdate_year2011 0.2837055 0.1629079 1.742 0.08190 .
ORdate_year2012 0.2214491 0.1765169 1.255 0.20994
ORdate_year2013 0.4549845 0.6171648 0.737 0.46116
Hypertension.compositeyes -0.1993553 0.0881356 -2.262 0.02392 *
DiabetesStatusDiabetes 0.0036775 0.0706538 0.052 0.95850
SmokerStatusEx-smoker -0.0176436 0.0665778 -0.265 0.79106
SmokerStatusNever smoked 0.1526914 0.0943213 1.619 0.10580
Med.Statin.LLDyes -0.1855879 0.0714528 -2.597 0.00953 **
Med.all.antiplateletyes -0.0370170 0.0992669 -0.373 0.70930
GFR_MDRD -0.0008922 0.0015464 -0.577 0.56410
BMI -0.0031435 0.0080516 -0.390 0.69631
MedHx_CVDyes -0.0129803 0.0607599 -0.214 0.83088
stenose50-70% 0.0950319 0.3946953 0.241 0.80978
stenose70-90% 0.1776413 0.3789758 0.469 0.63936
stenose90-99% 0.1794213 0.3789767 0.473 0.63601
stenose100% (Occlusion) -0.1705767 0.4865559 -0.351 0.72598
stenose50-99% -0.0423183 0.6003672 -0.070 0.94382
stenose70-99% -0.1535183 0.5736901 -0.268 0.78906
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9118 on 991 degrees of freedom
Multiple R-squared: 0.1996, Adjusted R-squared: 0.1762
F-statistic: 8.524 on 29 and 991 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.071449
Standard error............: 0.032287
Odds ratio (effect size)..: 1.074
Lower 95% CI..............: 1.008
Upper 95% CI..............: 1.144
T-value...................: 2.212951
P-value...................: 0.02712827
R^2.......................: 0.199643
Adjusted r^2..............: 0.176221
Sample size of AE DB......: 2423
Sample size of model......: 1021
Missing data %............: 57.86215
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
ORdate_year + Hypertension.composite + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.998989 -0.082841 -0.005194 -0.195467 -0.885791 -1.045367 -0.644912
ORdate_year2007 ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013
-0.284799 -0.114170 -0.095530 0.195915 0.121543 0.044567 0.160029
Hypertension.compositeyes Med.Statin.LLDyes
-0.178391 -0.181218
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0462 -0.6138 0.0012 0.5487 3.2134
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.0195642 0.5692342 1.791 0.0736 .
currentDF[, TRAIT] -0.0736531 0.0324488 -2.270 0.0234 *
Age -0.0066320 0.0036519 -1.816 0.0697 .
Gendermale 0.0750529 0.0655997 1.144 0.2529
ORdate_year2003 -0.2036133 0.1591785 -1.279 0.2011
ORdate_year2004 -0.8868077 0.1536475 -5.772 1.05e-08 ***
ORdate_year2005 -1.0504518 0.1564695 -6.713 3.20e-11 ***
ORdate_year2006 -0.6586053 0.1562597 -4.215 2.73e-05 ***
ORdate_year2007 -0.3055708 0.1596241 -1.914 0.0559 .
ORdate_year2008 -0.1261514 0.1707578 -0.739 0.4602
ORdate_year2009 -0.0957263 0.1653406 -0.579 0.5627
ORdate_year2010 0.2027124 0.1682666 1.205 0.2286
ORdate_year2011 0.1293405 0.1644857 0.786 0.4319
ORdate_year2012 0.0736212 0.1781320 0.413 0.6795
ORdate_year2013 0.3183372 0.6184788 0.515 0.6069
Hypertension.compositeyes -0.1908082 0.0883576 -2.159 0.0311 *
DiabetesStatusDiabetes 0.0001618 0.0707683 0.002 0.9982
SmokerStatusEx-smoker -0.0132394 0.0668110 -0.198 0.8430
SmokerStatusNever smoked 0.1459261 0.0946233 1.542 0.1234
Med.Statin.LLDyes -0.1792528 0.0716336 -2.502 0.0125 *
Med.all.antiplateletyes -0.0435610 0.0994533 -0.438 0.6615
GFR_MDRD -0.0007295 0.0015518 -0.470 0.6384
BMI -0.0033816 0.0080771 -0.419 0.6756
MedHx_CVDyes -0.0085009 0.0609110 -0.140 0.8890
stenose50-70% 0.1377847 0.3954829 0.348 0.7276
stenose70-90% 0.2197527 0.3796531 0.579 0.5628
stenose90-99% 0.2207388 0.3797433 0.581 0.5612
stenose100% (Occlusion) -0.1650645 0.4873011 -0.339 0.7349
stenose50-99% 0.0538033 0.6016966 0.089 0.9288
stenose70-99% -0.0483338 0.5746272 -0.084 0.9330
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9131 on 987 degrees of freedom
Multiple R-squared: 0.1999, Adjusted R-squared: 0.1764
F-statistic: 8.503 on 29 and 987 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.073653
Standard error............: 0.032449
Odds ratio (effect size)..: 0.929
Lower 95% CI..............: 0.872
Upper 95% CI..............: 0.99
T-value...................: -2.269821
P-value...................: 0.02343342
R^2.......................: 0.199901
Adjusted r^2..............: 0.176393
Sample size of AE DB......: 2423
Sample size of model......: 1017
Missing data %............: 58.02724
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Hypertension.composite + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 ORdate_year2007
0.60939 -0.05724 -0.14049 -0.81904 -0.99237 -0.64200 -0.28490
ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013 Hypertension.compositeyes
-0.07093 -0.12584 0.25454 0.15669 0.06899 0.15108 -0.18103
Med.Statin.LLDyes
-0.17248
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0717 -0.6390 -0.0009 0.5507 3.2045
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.916784 0.604167 1.517 0.1295
currentDF[, TRAIT] -0.057078 0.031633 -1.804 0.0715 .
Age -0.004631 0.003778 -1.226 0.2206
Gendermale 0.113919 0.067303 1.693 0.0909 .
ORdate_year2003 -0.158897 0.162113 -0.980 0.3273
ORdate_year2004 -0.827564 0.153991 -5.374 9.75e-08 ***
ORdate_year2005 -1.005432 0.158303 -6.351 3.35e-10 ***
ORdate_year2006 -0.654848 0.160138 -4.089 4.70e-05 ***
ORdate_year2007 -0.299091 0.166598 -1.795 0.0729 .
ORdate_year2008 -0.085881 0.176736 -0.486 0.6271
ORdate_year2009 -0.128679 0.170067 -0.757 0.4495
ORdate_year2010 0.256588 0.170948 1.501 0.1337
ORdate_year2011 0.162512 0.167120 0.972 0.3311
ORdate_year2012 0.081555 0.182961 0.446 0.6559
ORdate_year2013 0.547786 0.671319 0.816 0.4147
Hypertension.compositeyes -0.192092 0.092221 -2.083 0.0375 *
DiabetesStatusDiabetes -0.003273 0.075664 -0.043 0.9655
SmokerStatusEx-smoker -0.016265 0.069837 -0.233 0.8159
SmokerStatusNever smoked 0.155670 0.098792 1.576 0.1154
Med.Statin.LLDyes -0.178429 0.074077 -2.409 0.0162 *
Med.all.antiplateletyes -0.024698 0.105698 -0.234 0.8153
GFR_MDRD -0.001080 0.001631 -0.663 0.5078
BMI -0.002124 0.008423 -0.252 0.8009
MedHx_CVDyes -0.014189 0.063545 -0.223 0.8234
stenose50-70% 0.033108 0.436263 0.076 0.9395
stenose70-90% 0.116802 0.419514 0.278 0.7807
stenose90-99% 0.108512 0.419068 0.259 0.7957
stenose100% (Occlusion) -0.283483 0.520604 -0.545 0.5862
stenose50-99% 0.011439 0.630629 0.018 0.9855
stenose70-99% -0.516537 0.705916 -0.732 0.4645
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9223 on 923 degrees of freedom
Multiple R-squared: 0.2, Adjusted R-squared: 0.1749
F-statistic: 7.957 on 29 and 923 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.057078
Standard error............: 0.031633
Odds ratio (effect size)..: 0.945
Lower 95% CI..............: 0.888
Upper 95% CI..............: 1.005
T-value...................: -1.804375
P-value...................: 0.07149835
R^2.......................: 0.199996
Adjusted r^2..............: 0.174861
Sample size of AE DB......: 2423
Sample size of model......: 953
Missing data %............: 60.66859
Analysis of MCP1_pg_ml_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.05751 0.08555 0.30162 -0.17288 -0.67562 -0.69578 -0.20651
ORdate_year2007 ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013
-0.08387 0.35081 0.38963 0.52372 0.67614 0.54124 0.96232
Hypertension.compositeyes Med.Statin.LLDyes
-0.13378 -0.18835
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.81361 -0.56013 -0.00965 0.57520 3.03672
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.521e-01 5.416e-01 0.281 0.778893
currentDF[, TRAIT] 8.702e-02 3.088e-02 2.818 0.004935 **
Age 2.581e-03 3.462e-03 0.746 0.456057
Gendermale 3.238e-01 6.169e-02 5.248 1.88e-07 ***
ORdate_year2003 -1.795e-01 1.505e-01 -1.192 0.233381
ORdate_year2004 -6.759e-01 1.440e-01 -4.695 3.05e-06 ***
ORdate_year2005 -7.101e-01 1.469e-01 -4.835 1.55e-06 ***
ORdate_year2006 -2.257e-01 1.495e-01 -1.510 0.131409
ORdate_year2007 -1.094e-01 1.522e-01 -0.719 0.472314
ORdate_year2008 3.269e-01 1.613e-01 2.027 0.042968 *
ORdate_year2009 3.858e-01 1.556e-01 2.479 0.013350 *
ORdate_year2010 5.094e-01 1.583e-01 3.218 0.001333 **
ORdate_year2011 6.667e-01 1.558e-01 4.278 2.07e-05 ***
ORdate_year2012 5.646e-01 1.688e-01 3.344 0.000858 ***
ORdate_year2013 1.209e+00 5.903e-01 2.048 0.040806 *
Hypertension.compositeyes -1.405e-01 8.430e-02 -1.666 0.096014 .
DiabetesStatusDiabetes -5.254e-03 6.758e-02 -0.078 0.938043
SmokerStatusEx-smoker -4.518e-02 6.368e-02 -0.709 0.478244
SmokerStatusNever smoked 5.872e-02 9.022e-02 0.651 0.515292
Med.Statin.LLDyes -1.814e-01 6.835e-02 -2.655 0.008063 **
Med.all.antiplateletyes 3.637e-02 9.495e-02 0.383 0.701785
GFR_MDRD -2.493e-05 1.479e-03 -0.017 0.986556
BMI -3.909e-03 7.702e-03 -0.508 0.611907
MedHx_CVDyes -2.267e-02 5.812e-02 -0.390 0.696530
stenose50-70% -3.105e-01 3.775e-01 -0.823 0.410977
stenose70-90% -1.469e-01 3.625e-01 -0.405 0.685484
stenose90-99% -1.803e-01 3.625e-01 -0.497 0.619126
stenose100% (Occlusion) -3.730e-01 4.654e-01 -0.801 0.423069
stenose50-99% -4.982e-01 5.743e-01 -0.867 0.385900
stenose70-99% -5.429e-01 5.487e-01 -0.989 0.322699
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8721 on 991 degrees of freedom
Multiple R-squared: 0.2516, Adjusted R-squared: 0.2297
F-statistic: 11.49 on 29 and 991 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.087015
Standard error............: 0.030883
Odds ratio (effect size)..: 1.091
Lower 95% CI..............: 1.027
Upper 95% CI..............: 1.159
T-value...................: 2.817586
P-value...................: 0.004934803
R^2.......................: 0.251598
Adjusted r^2..............: 0.229697
Sample size of AE DB......: 2423
Sample size of model......: 1021
Missing data %............: 57.86215
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.2160 -0.1478 0.2664 -0.2867 -0.8629 -0.8287 -0.2167
ORdate_year2007 ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013
-0.1274 0.1895 0.2146 0.2497 0.4088 0.3008 0.7825
Hypertension.compositeyes Med.Statin.LLDyes
-0.1333 -0.1697
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.74231 -0.54954 -0.01639 0.57832 3.04631
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.4361613 0.5402879 0.807 0.41970
currentDF[, TRAIT] -0.1450187 0.0307988 -4.709 2.85e-06 ***
Age -0.0004735 0.0034662 -0.137 0.89137
Gendermale 0.2859822 0.0622638 4.593 4.93e-06 ***
ORdate_year2003 -0.2963179 0.1510841 -1.961 0.05013 .
ORdate_year2004 -0.8582826 0.1458343 -5.885 5.44e-09 ***
ORdate_year2005 -0.8363289 0.1485128 -5.631 2.33e-08 ***
ORdate_year2006 -0.2280818 0.1483137 -1.538 0.12441
ORdate_year2007 -0.1411287 0.1515070 -0.931 0.35182
ORdate_year2008 0.1793360 0.1620745 1.107 0.26878
ORdate_year2009 0.2199681 0.1569328 1.402 0.16133
ORdate_year2010 0.2475414 0.1597100 1.550 0.12148
ORdate_year2011 0.4137207 0.1561214 2.650 0.00818 **
ORdate_year2012 0.3274651 0.1690737 1.937 0.05305 .
ORdate_year2013 0.9859325 0.5870283 1.680 0.09336 .
Hypertension.compositeyes -0.1265208 0.0838645 -1.509 0.13171
DiabetesStatusDiabetes -0.0062101 0.0671696 -0.092 0.92636
SmokerStatusEx-smoker -0.0407440 0.0634135 -0.643 0.52069
SmokerStatusNever smoked 0.0389968 0.0898115 0.434 0.66423
Med.Statin.LLDyes -0.1711426 0.0679909 -2.517 0.01199 *
Med.all.antiplateletyes 0.0232340 0.0943960 0.246 0.80563
GFR_MDRD 0.0003061 0.0014729 0.208 0.83538
BMI -0.0044116 0.0076664 -0.575 0.56512
MedHx_CVDyes -0.0192489 0.0578136 -0.333 0.73924
stenose50-70% -0.2340854 0.3753720 -0.624 0.53303
stenose70-90% -0.0762249 0.3603472 -0.212 0.83252
stenose90-99% -0.1053537 0.3604328 -0.292 0.77012
stenose100% (Occlusion) -0.3448768 0.4625211 -0.746 0.45606
stenose50-99% -0.3404183 0.5710995 -0.596 0.55126
stenose70-99% -0.3770504 0.5454066 -0.691 0.48953
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8666 on 987 degrees of freedom
Multiple R-squared: 0.2617, Adjusted R-squared: 0.24
F-statistic: 12.06 on 29 and 987 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.145019
Standard error............: 0.030799
Odds ratio (effect size)..: 0.865
Lower 95% CI..............: 0.814
Upper 95% CI..............: 0.919
T-value...................: -4.708586
P-value...................: 2.850934e-06
R^2.......................: 0.261683
Adjusted r^2..............: 0.23999
Sample size of AE DB......: 2423
Sample size of model......: 1017
Missing data %............: 58.02724
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.09111 -0.04559 0.31468 -0.20102 -0.72256 -0.70360 -0.18099
ORdate_year2007 ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013
-0.09677 0.32031 0.27294 0.36129 0.55395 0.40912 0.87216
Hypertension.compositeyes Med.Statin.LLDyes
-0.12511 -0.18896
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.71018 -0.56067 -0.00106 0.54787 3.14928
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.1982784 0.5784680 0.343 0.731853
currentDF[, TRAIT] -0.0476932 0.0302878 -1.575 0.115675
Age 0.0024230 0.0036174 0.670 0.503137
Gendermale 0.3351086 0.0644399 5.200 2.45e-07 ***
ORdate_year2003 -0.2036561 0.1552174 -1.312 0.189823
ORdate_year2004 -0.7157084 0.1474413 -4.854 1.42e-06 ***
ORdate_year2005 -0.7109880 0.1515690 -4.691 3.13e-06 ***
ORdate_year2006 -0.1937282 0.1533260 -1.264 0.206727
ORdate_year2007 -0.1127757 0.1595118 -0.707 0.479742
ORdate_year2008 0.3081827 0.1692189 1.821 0.068899 .
ORdate_year2009 0.2749185 0.1628333 1.688 0.091683 .
ORdate_year2010 0.3438151 0.1636768 2.101 0.035949 *
ORdate_year2011 0.5531725 0.1600117 3.457 0.000571 ***
ORdate_year2012 0.4281871 0.1751791 2.444 0.014700 *
ORdate_year2013 1.3706779 0.6427637 2.132 0.033231 *
Hypertension.compositeyes -0.1388259 0.0882982 -1.572 0.116238
DiabetesStatusDiabetes -0.0176177 0.0724453 -0.243 0.807915
SmokerStatusEx-smoker -0.0372387 0.0668667 -0.557 0.577725
SmokerStatusNever smoked 0.0394769 0.0945897 0.417 0.676521
Med.Statin.LLDyes -0.1865186 0.0709258 -2.630 0.008686 **
Med.all.antiplateletyes 0.0562512 0.1012018 0.556 0.578461
GFR_MDRD -0.0001144 0.0015613 -0.073 0.941608
BMI -0.0014019 0.0080645 -0.174 0.862030
MedHx_CVDyes -0.0043239 0.0608421 -0.071 0.943359
stenose50-70% -0.4284908 0.4177068 -1.026 0.305247
stenose70-90% -0.2212811 0.4016695 -0.551 0.581833
stenose90-99% -0.2698431 0.4012431 -0.673 0.501423
stenose100% (Occlusion) -0.5034116 0.4984594 -1.010 0.312791
stenose50-99% -0.4677575 0.6038047 -0.775 0.438725
stenose70-99% -1.0125141 0.6758899 -1.498 0.134463
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8831 on 923 degrees of freedom
Multiple R-squared: 0.2438, Adjusted R-squared: 0.2201
F-statistic: 10.26 on 29 and 923 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.047693
Standard error............: 0.030288
Odds ratio (effect size)..: 0.953
Lower 95% CI..............: 0.898
Upper 95% CI..............: 1.012
T-value...................: -1.574671
P-value...................: 0.1156752
R^2.......................: 0.243832
Adjusted r^2..............: 0.220074
Sample size of AE DB......: 2423
Sample size of model......: 953
Missing data %............: 60.66859
Analysis of MCP1_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.3979 0.1008 0.2859 -0.1474 -0.5018 -0.5602 -1.0479
Hypertension.compositeyes
-0.2376
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3562 -0.5959 0.0149 0.6483 2.5981
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.4821645 0.8514070 1.741 0.082358 .
currentDF[, TRAIT] 0.0971796 0.0418936 2.320 0.020781 *
Age -0.0091883 0.0057819 -1.589 0.112691
Gendermale 0.3074252 0.1006537 3.054 0.002382 **
ORdate_year2003 -0.1601588 0.1529437 -1.047 0.295550
ORdate_year2004 -0.4754971 0.1487139 -3.197 0.001479 **
ORdate_year2005 -0.5411177 0.1503567 -3.599 0.000353 ***
ORdate_year2006 -0.9814467 0.2510957 -3.909 0.000106 ***
Hypertension.compositeyes -0.2381638 0.1336676 -1.782 0.075426 .
DiabetesStatusDiabetes -0.0739786 0.1125393 -0.657 0.511269
SmokerStatusEx-smoker 0.0903938 0.1000648 0.903 0.366796
SmokerStatusNever smoked 0.2636848 0.1478240 1.784 0.075097 .
Med.Statin.LLDyes -0.1427243 0.1038325 -1.375 0.169914
Med.all.antiplateletyes 0.1359664 0.1588797 0.856 0.392549
GFR_MDRD -0.0005251 0.0025052 -0.210 0.834069
BMI -0.0120999 0.0119227 -1.015 0.310689
MedHx_CVDyes 0.0196800 0.0937526 0.210 0.833824
stenose50-70% -0.3794169 0.6208752 -0.611 0.541425
stenose70-90% -0.2357040 0.5757624 -0.409 0.682447
stenose90-99% -0.2104738 0.5745091 -0.366 0.714264
stenose100% (Occlusion) -0.8999190 0.7285330 -1.235 0.217348
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9732 on 476 degrees of freedom
Multiple R-squared: 0.1149, Adjusted R-squared: 0.07774
F-statistic: 3.09 on 20 and 476 DF, p-value: 9.477e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.09718
Standard error............: 0.041894
Odds ratio (effect size)..: 1.102
Lower 95% CI..............: 1.015
Upper 95% CI..............: 1.196
T-value...................: 2.319675
P-value...................: 0.02078069
R^2.......................: 0.114927
Adjusted r^2..............: 0.077739
Sample size of AE DB......: 2423
Sample size of model......: 497
Missing data %............: 79.48824
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005
1.41460 -0.24916 -0.01152 0.23861 -0.34985 -0.81202 -0.77810
ORdate_year2006 Hypertension.compositeyes
-1.11511 -0.19628
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.10475 -0.60146 -0.00887 0.65289 2.65311
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.1619269 0.8293428 2.607 0.00943 **
currentDF[, TRAIT] -0.2530307 0.0438266 -5.773 1.41e-08 ***
Age -0.0155143 0.0056781 -2.732 0.00652 **
Gendermale 0.2435318 0.0985604 2.471 0.01383 *
ORdate_year2003 -0.3518833 0.1506015 -2.337 0.01988 *
ORdate_year2004 -0.7957806 0.1495069 -5.323 1.58e-07 ***
ORdate_year2005 -0.7680174 0.1496904 -5.131 4.22e-07 ***
ORdate_year2006 -1.0714294 0.2435906 -4.398 1.35e-05 ***
Hypertension.compositeyes -0.2019451 0.1285130 -1.571 0.11676
DiabetesStatusDiabetes -0.0753820 0.1090043 -0.692 0.48956
SmokerStatusEx-smoker 0.1253294 0.0968709 1.294 0.19637
SmokerStatusNever smoked 0.2412122 0.1428876 1.688 0.09204 .
Med.Statin.LLDyes -0.1362649 0.1008245 -1.352 0.17718
Med.all.antiplateletyes 0.1162991 0.1538168 0.756 0.44997
GFR_MDRD -0.0005452 0.0024244 -0.225 0.82216
BMI -0.0109956 0.0115294 -0.954 0.34072
MedHx_CVDyes 0.0229221 0.0910101 0.252 0.80126
stenose50-70% -0.2765773 0.6012858 -0.460 0.64574
stenose70-90% -0.2447519 0.5573641 -0.439 0.66077
stenose90-99% -0.2483737 0.5558975 -0.447 0.65523
stenose100% (Occlusion) -1.0468909 0.7050939 -1.485 0.13827
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.942 on 474 degrees of freedom
Multiple R-squared: 0.1641, Adjusted R-squared: 0.1288
F-statistic: 4.653 on 20 and 474 DF, p-value: 2.953e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.253031
Standard error............: 0.043827
Odds ratio (effect size)..: 0.776
Lower 95% CI..............: 0.713
Upper 95% CI..............: 0.846
T-value...................: -5.773444
P-value...................: 1.405968e-08
R^2.......................: 0.164109
Adjusted r^2..............: 0.128839
Sample size of AE DB......: 2423
Sample size of model......: 495
Missing data %............: 79.57078
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.2627 0.3159 -0.1958 -0.6161 -0.6257 -1.0273
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3536 -0.6182 -0.0129 0.6137 2.5073
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.4236511 0.8578719 1.660 0.097685 .
currentDF[, TRAIT] -0.0428541 0.0551328 -0.777 0.437383
Age -0.0095064 0.0058671 -1.620 0.105844
Gendermale 0.3344393 0.1017990 3.285 0.001096 **
ORdate_year2003 -0.2193136 0.1555980 -1.409 0.159358
ORdate_year2004 -0.5681802 0.1507496 -3.769 0.000185 ***
ORdate_year2005 -0.5879279 0.1545027 -3.805 0.000161 ***
ORdate_year2006 -0.9626971 0.2578178 -3.734 0.000212 ***
Hypertension.compositeyes -0.1676466 0.1359507 -1.233 0.218144
DiabetesStatusDiabetes -0.0381932 0.1148173 -0.333 0.739553
SmokerStatusEx-smoker 0.0991641 0.1015252 0.977 0.329203
SmokerStatusNever smoked 0.2648466 0.1497335 1.769 0.077584 .
Med.Statin.LLDyes -0.1438781 0.1053398 -1.366 0.172646
Med.all.antiplateletyes 0.1375951 0.1616640 0.851 0.395141
GFR_MDRD 0.0002032 0.0025703 0.079 0.937019
BMI -0.0103943 0.0120946 -0.859 0.390553
MedHx_CVDyes 0.0335497 0.0952761 0.352 0.724899
stenose50-70% -0.4598005 0.6231621 -0.738 0.460977
stenose70-90% -0.2515519 0.5782964 -0.435 0.663773
stenose90-99% -0.2452452 0.5769675 -0.425 0.670990
stenose100% (Occlusion) -0.9637052 0.7316084 -1.317 0.188405
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.977 on 466 degrees of freedom
Multiple R-squared: 0.1095, Adjusted R-squared: 0.07127
F-statistic: 2.865 on 20 and 466 DF, p-value: 4.012e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.042854
Standard error............: 0.055133
Odds ratio (effect size)..: 0.958
Lower 95% CI..............: 0.86
Upper 95% CI..............: 1.067
T-value...................: -0.777288
P-value...................: 0.4373834
R^2.......................: 0.10949
Adjusted r^2..............: 0.071271
Sample size of AE DB......: 2423
Sample size of model......: 487
Missing data %............: 79.90095 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Binary plaque traits
Analysis of binary plaque traits as a function of plasma/plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.BIN)) {
TRAIT = TRAITS.BIN[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year +
Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ug_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + ORdate_year + DiabetesStatus + SmokerStatus + BMI,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-2.38108 -0.28459 0.02949 0.15193 0.24414 0.59237 0.62402
ORdate_year2007 ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013
0.18675 0.12180 -0.16390 -1.04042 -1.42426 -0.94357 -1.40585
DiabetesStatusDiabetes SmokerStatusEx-smoker SmokerStatusNever smoked BMI
-0.26016 -0.47452 -0.58405 0.02740
Degrees of Freedom: 1025 Total (i.e. Null); 1008 Residual
Null Deviance: 1420
Residual Deviance: 1269 AIC: 1305
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8667 -1.0558 -0.5198 1.0389 2.2630
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.287e+00 1.395e+00 -1.640 0.101082
currentDF[, PROTEIN] -2.777e-01 7.499e-02 -3.704 0.000213 ***
Age 2.820e-02 8.785e-03 3.210 0.001327 **
Gendermale -1.213e-01 1.528e-01 -0.794 0.427040
ORdate_year2003 2.010e-01 3.532e-01 0.569 0.569319
ORdate_year2004 2.985e-01 3.431e-01 0.870 0.384305
ORdate_year2005 6.538e-01 3.593e-01 1.820 0.068769 .
ORdate_year2006 6.897e-01 3.597e-01 1.917 0.055193 .
ORdate_year2007 2.667e-01 3.588e-01 0.743 0.457270
ORdate_year2008 1.814e-01 3.794e-01 0.478 0.632561
ORdate_year2009 -3.800e-02 3.654e-01 -0.104 0.917165
ORdate_year2010 -9.539e-01 3.906e-01 -2.442 0.014592 *
ORdate_year2011 -1.335e+00 3.955e-01 -3.375 0.000739 ***
ORdate_year2012 -7.397e-01 4.071e-01 -1.817 0.069232 .
ORdate_year2013 -1.336e+00 1.241e+00 -1.077 0.281691
Hypertension.compositeyes 2.762e-01 2.101e-01 1.314 0.188686
DiabetesStatusDiabetes -2.573e-01 1.686e-01 -1.526 0.127012
SmokerStatusEx-smoker -4.538e-01 1.590e-01 -2.854 0.004321 **
SmokerStatusNever smoked -6.077e-01 2.251e-01 -2.699 0.006948 **
Med.Statin.LLDyes -6.916e-02 1.684e-01 -0.411 0.681346
Med.all.antiplateletyes -1.364e-02 2.356e-01 -0.058 0.953846
GFR_MDRD 8.597e-04 3.713e-03 0.232 0.816884
BMI 2.561e-02 1.922e-02 1.333 0.182638
MedHx_CVDyes 2.562e-02 1.432e-01 0.179 0.858023
stenose50-70% -6.395e-01 1.006e+00 -0.636 0.525010
stenose70-90% -2.342e-01 9.658e-01 -0.243 0.808369
stenose90-99% -1.380e-01 9.661e-01 -0.143 0.886446
stenose100% (Occlusion) 9.672e-01 1.295e+00 0.747 0.455139
stenose50-99% -1.402e+01 4.264e+02 -0.033 0.973768
stenose70-99% -1.791e-01 1.371e+00 -0.131 0.896086
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1420.3 on 1025 degrees of freedom
Residual deviance: 1258.6 on 996 degrees of freedom
AIC: 1318.6
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.27771
Standard error............: 0.074985
Odds ratio (effect size)..: 0.758
Lower 95% CI..............: 0.654
Upper 95% CI..............: 0.877
Z-value...................: -3.703527
P-value...................: 0.0002126226
Hosmer and Lemeshow r^2...: 0.113835
Cox and Snell r^2.........: 0.145791
Nagelkerke's pseudo r^2...: 0.194518
Sample size of AE DB......: 2423
Sample size of model......: 1026
Missing data %............: 57.6558
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
ORdate_year + SmokerStatus + BMI + MedHx_CVD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 ORdate_year2007
0.34659 -0.17487 -0.43906 -0.01311 1.22116 1.04284 -0.29089
ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013 SmokerStatusEx-smoker
0.56421 -0.29663 0.35290 0.07619 0.47573 -0.43502 -0.41295
SmokerStatusNever smoked BMI MedHx_CVDyes
-0.73011 0.03828 0.26765
Degrees of Freedom: 1026 Total (i.e. Null); 1010 Residual
Null Deviance: 1049
Residual Deviance: 986.1 AIC: 1020
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3728 0.3300 0.5831 0.7329 1.2326
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.356e+01 9.053e+02 0.015 0.988052
currentDF[, PROTEIN] -1.534e-01 8.950e-02 -1.714 0.086546 .
Age 1.384e-02 1.017e-02 1.362 0.173354
Gendermale -7.545e-02 1.808e-01 -0.417 0.676429
ORdate_year2003 -3.352e-01 4.040e-01 -0.830 0.406650
ORdate_year2004 2.685e-02 4.066e-01 0.066 0.947338
ORdate_year2005 1.275e+00 4.957e-01 2.572 0.010107 *
ORdate_year2006 1.104e+00 4.823e-01 2.289 0.022099 *
ORdate_year2007 -2.835e-01 4.121e-01 -0.688 0.491592
ORdate_year2008 5.413e-01 4.714e-01 1.148 0.250854
ORdate_year2009 -3.296e-01 4.161e-01 -0.792 0.428187
ORdate_year2010 3.196e-01 4.426e-01 0.722 0.470284
ORdate_year2011 8.227e-03 4.212e-01 0.020 0.984418
ORdate_year2012 3.376e-01 4.724e-01 0.715 0.474827
ORdate_year2013 -8.169e-01 1.104e+00 -0.740 0.459212
Hypertension.compositeyes 2.717e-01 2.352e-01 1.155 0.247925
DiabetesStatusDiabetes 6.637e-02 2.024e-01 0.328 0.743046
SmokerStatusEx-smoker -4.784e-01 1.944e-01 -2.461 0.013860 *
SmokerStatusNever smoked -8.717e-01 2.590e-01 -3.366 0.000762 ***
Med.Statin.LLDyes -1.115e-02 1.976e-01 -0.056 0.955023
Med.all.antiplateletyes 2.663e-01 2.672e-01 0.997 0.318796
GFR_MDRD 3.752e-03 4.396e-03 0.854 0.393327
BMI 4.072e-02 2.402e-02 1.695 0.090026 .
MedHx_CVDyes 2.485e-01 1.678e-01 1.481 0.138646
stenose50-70% -1.445e+01 9.053e+02 -0.016 0.987261
stenose70-90% -1.485e+01 9.053e+02 -0.016 0.986915
stenose90-99% -1.496e+01 9.053e+02 -0.017 0.986814
stenose100% (Occlusion) 3.567e-01 1.177e+03 0.000 0.999758
stenose50-99% 2.246e-02 1.481e+03 0.000 0.999988
stenose70-99% -1.401e+01 9.053e+02 -0.015 0.987656
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1048.55 on 1026 degrees of freedom
Residual deviance: 971.98 on 997 degrees of freedom
AIC: 1032
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.153386
Standard error............: 0.089495
Odds ratio (effect size)..: 0.858
Lower 95% CI..............: 0.72
Upper 95% CI..............: 1.022
Z-value...................: -1.713904
P-value...................: 0.08654644
Hosmer and Lemeshow r^2...: 0.073029
Cox and Snell r^2.........: 0.071849
Nagelkerke's pseudo r^2...: 0.112307
Sample size of AE DB......: 2423
Sample size of model......: 1027
Missing data %............: 57.61453
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + SmokerStatus, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005
-0.70521 0.45974 0.01687 1.00984 0.75666 0.95568 0.80536
ORdate_year2006 ORdate_year2007 ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012
0.64927 0.22452 -0.72687 -0.43512 -0.29927 -1.07426 -1.28144
ORdate_year2013 SmokerStatusEx-smoker SmokerStatusNever smoked
-0.67356 -0.31131 0.27838
Degrees of Freedom: 1026 Total (i.e. Null); 1010 Residual
Null Deviance: 1209
Residual Deviance: 1076 AIC: 1110
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.7057 -0.9270 0.5682 0.7863 1.6312
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 13.455449 554.513532 0.024 0.98064
currentDF[, PROTEIN] 0.461019 0.089249 5.166 2.40e-07 ***
Age 0.019841 0.009478 2.093 0.03632 *
Gendermale 1.016637 0.165723 6.135 8.54e-10 ***
ORdate_year2003 0.790819 0.436557 1.811 0.07006 .
ORdate_year2004 0.998412 0.416252 2.399 0.01646 *
ORdate_year2005 0.817992 0.422981 1.934 0.05313 .
ORdate_year2006 0.675490 0.422717 1.598 0.11005
ORdate_year2007 0.265977 0.421647 0.631 0.52817
ORdate_year2008 -0.721520 0.424221 -1.701 0.08898 .
ORdate_year2009 -0.445387 0.412036 -1.081 0.27972
ORdate_year2010 -0.308070 0.421434 -0.731 0.46478
ORdate_year2011 -1.109260 0.404454 -2.743 0.00610 **
ORdate_year2012 -1.216768 0.431677 -2.819 0.00482 **
ORdate_year2013 -0.278746 1.158591 -0.241 0.80987
Hypertension.compositeyes 0.002770 0.234555 0.012 0.99058
DiabetesStatusDiabetes -0.245490 0.184369 -1.332 0.18302
SmokerStatusEx-smoker -0.327507 0.176804 -1.852 0.06397 .
SmokerStatusNever smoked 0.253071 0.260789 0.970 0.33184
Med.Statin.LLDyes -0.088495 0.194379 -0.455 0.64891
Med.all.antiplateletyes 0.159685 0.263076 0.607 0.54386
GFR_MDRD 0.002115 0.004069 0.520 0.60318
BMI 0.003836 0.020677 0.186 0.85282
MedHx_CVDyes 0.107392 0.159909 0.672 0.50185
stenose50-70% -14.570510 554.512549 -0.026 0.97904
stenose70-90% -14.676350 554.512480 -0.026 0.97888
stenose90-99% -14.760373 554.512485 -0.027 0.97876
stenose100% (Occlusion) -15.152699 554.512981 -0.027 0.97820
stenose50-99% -16.115248 554.513777 -0.029 0.97682
stenose70-99% -15.477149 554.513354 -0.028 0.97773
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1209.2 on 1026 degrees of freedom
Residual deviance: 1065.4 on 997 degrees of freedom
AIC: 1125.4
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.461019
Standard error............: 0.089249
Odds ratio (effect size)..: 1.586
Lower 95% CI..............: 1.331
Upper 95% CI..............: 1.889
Z-value...................: 5.16555
P-value...................: 2.397336e-07
Hosmer and Lemeshow r^2...: 0.11891
Cox and Snell r^2.........: 0.130648
Nagelkerke's pseudo r^2...: 0.188818
Sample size of AE DB......: 2423
Sample size of model......: 1027
Missing data %............: 57.61453
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + ORdate_year +
BMI + MedHx_CVD, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 ORdate_year2007 ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011
-0.28725 0.55342 -0.08820 0.04442 0.08371 -0.59131 -0.65083 -0.65420 -1.18209 -1.31694 -1.51164
ORdate_year2012 ORdate_year2013 BMI MedHx_CVDyes
-1.12787 -0.52134 0.02804 0.39584
Degrees of Freedom: 1024 Total (i.e. Null); 1010 Residual
Null Deviance: 1371
Residual Deviance: 1275 AIC: 1305
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0253 -1.1424 0.6745 0.9648 1.7097
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.499554 1.373083 -0.364 0.71599
currentDF[, PROTEIN] 0.054431 0.076157 0.715 0.47478
Age 0.010658 0.008563 1.245 0.21326
Gendermale 0.588920 0.150980 3.901 9.59e-05 ***
ORdate_year2003 -0.087142 0.399105 -0.218 0.82716
ORdate_year2004 0.110600 0.390268 0.283 0.77687
ORdate_year2005 0.172520 0.401993 0.429 0.66781
ORdate_year2006 -0.534439 0.389281 -1.373 0.16979
ORdate_year2007 -0.591044 0.392396 -1.506 0.13200
ORdate_year2008 -0.610505 0.412007 -1.482 0.13840
ORdate_year2009 -1.148955 0.395814 -2.903 0.00370 **
ORdate_year2010 -1.314552 0.400155 -3.285 0.00102 **
ORdate_year2011 -1.508299 0.394113 -3.827 0.00013 ***
ORdate_year2012 -1.250629 0.420674 -2.973 0.00295 **
ORdate_year2013 -1.355405 1.092280 -1.241 0.21464
Hypertension.compositeyes -0.135897 0.209010 -0.650 0.51557
DiabetesStatusDiabetes -0.125530 0.166937 -0.752 0.45207
SmokerStatusEx-smoker -0.103661 0.159294 -0.651 0.51521
SmokerStatusNever smoked -0.172230 0.220544 -0.781 0.43484
Med.Statin.LLDyes -0.107013 0.171323 -0.625 0.53222
Med.all.antiplateletyes 0.115957 0.234589 0.494 0.62110
GFR_MDRD -0.002960 0.003661 -0.809 0.41878
BMI 0.035807 0.019190 1.866 0.06205 .
MedHx_CVDyes 0.386769 0.142613 2.712 0.00669 **
stenose50-70% -0.423616 0.978058 -0.433 0.66493
stenose70-90% -0.385762 0.945021 -0.408 0.68312
stenose90-99% -0.317154 0.945864 -0.335 0.73739
stenose100% (Occlusion) -0.808055 1.167932 -0.692 0.48902
stenose50-99% -0.225957 1.397976 -0.162 0.87160
stenose70-99% 1.568848 1.521576 1.031 0.30251
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1371.2 on 1024 degrees of freedom
Residual deviance: 1264.9 on 995 degrees of freedom
AIC: 1324.9
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IPH
Effect size...............: 0.054431
Standard error............: 0.076157
Odds ratio (effect size)..: 1.056
Lower 95% CI..............: 0.91
Upper 95% CI..............: 1.226
Z-value...................: 0.714726
P-value...................: 0.4747782
Hosmer and Lemeshow r^2...: 0.077488
Cox and Snell r^2.........: 0.098465
Nagelkerke's pseudo r^2...: 0.133502
Sample size of AE DB......: 2423
Sample size of model......: 1025
Missing data %............: 57.69707
- processing MAC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + ORdate_year + Med.Statin.LLD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 ORdate_year2007
-0.965253 0.291269 0.553005 0.134726 0.736078 1.349719 1.404837 0.960290
ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013 Med.Statin.LLDyes
-0.266631 0.391323 -0.002592 -0.441056 -1.119532 0.138922 0.296235
Degrees of Freedom: 1022 Total (i.e. Null); 1008 Residual
Null Deviance: 1417
Residual Deviance: 1296 AIC: 1326
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.948 -1.067 0.605 1.031 2.016
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.1434405 1.3826620 0.827 0.408246
currentDF[, PROTEIN] 0.2850532 0.0757349 3.764 0.000167 ***
Age -0.0108504 0.0084853 -1.279 0.200992
Gendermale 0.5961812 0.1519559 3.923 8.73e-05 ***
ORdate_year2003 0.1486866 0.3649392 0.407 0.683693
ORdate_year2004 0.7329459 0.3540903 2.070 0.038458 *
ORdate_year2005 1.3490733 0.3745811 3.602 0.000316 ***
ORdate_year2006 1.3953549 0.3766539 3.705 0.000212 ***
ORdate_year2007 0.9441096 0.3726751 2.533 0.011298 *
ORdate_year2008 -0.2943364 0.3920852 -0.751 0.452836
ORdate_year2009 0.3452511 0.3733217 0.925 0.355065
ORdate_year2010 0.0003512 0.3743448 0.001 0.999251
ORdate_year2011 -0.4791970 0.3729712 -1.285 0.198859
ORdate_year2012 -1.3956939 0.4488404 -3.110 0.001874 **
ORdate_year2013 -0.8923112 1.0750516 -0.830 0.406529
Hypertension.compositeyes 0.0121590 0.2092154 0.058 0.953656
DiabetesStatusDiabetes -0.1311948 0.1661607 -0.790 0.429781
SmokerStatusEx-smoker 0.0834341 0.1565114 0.533 0.593974
SmokerStatusNever smoked 0.4210810 0.2234223 1.885 0.059472 .
Med.Statin.LLDyes 0.2899997 0.1667161 1.739 0.081950 .
Med.all.antiplateletyes -0.3332375 0.2366651 -1.408 0.159115
GFR_MDRD -0.0008101 0.0036227 -0.224 0.823048
BMI -0.0152345 0.0189620 -0.803 0.421730
MedHx_CVDyes 0.1895065 0.1427898 1.327 0.184452
stenose50-70% -0.7233968 1.0050697 -0.720 0.471680
stenose70-90% -0.7863134 0.9727982 -0.808 0.418918
stenose90-99% -0.8599538 0.9733848 -0.883 0.376984
stenose100% (Occlusion) -1.7833693 1.2301895 -1.450 0.147150
stenose50-99% 0.8008175 1.4435633 0.555 0.579065
stenose70-99% 1.0078447 1.3390635 0.753 0.451661
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1417.5 on 1022 degrees of freedom
Residual deviance: 1279.6 on 993 degrees of freedom
AIC: 1339.6
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MAC_binned
Effect size...............: 0.285053
Standard error............: 0.075735
Odds ratio (effect size)..: 1.33
Lower 95% CI..............: 1.146
Upper 95% CI..............: 1.543
Z-value...................: 3.763827
P-value...................: 0.0001673329
Hosmer and Lemeshow r^2...: 0.09724
Cox and Snell r^2.........: 0.126053
Nagelkerke's pseudo r^2...: 0.16811
Sample size of AE DB......: 2423
Sample size of model......: 1023
Missing data %............: 57.77961
- processing SMC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + SmokerStatus, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale SmokerStatusEx-smoker SmokerStatusNever smoked
3.02431 -0.17162 -0.02586 -0.44826 -0.05776 -0.42741
Degrees of Freedom: 1022 Total (i.e. Null); 1017 Residual
Null Deviance: 1260
Residual Deviance: 1227 AIC: 1239
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0698 -1.2723 0.7144 0.8698 1.7084
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.092931 1.354833 1.545 0.12240
currentDF[, PROTEIN] -0.144893 0.078452 -1.847 0.06476 .
Age -0.024809 0.009119 -2.721 0.00651 **
Gendermale -0.480954 0.164654 -2.921 0.00349 **
ORdate_year2003 -0.242424 0.384488 -0.631 0.52836
ORdate_year2004 0.006674 0.376821 0.018 0.98587
ORdate_year2005 0.123183 0.391673 0.315 0.75314
ORdate_year2006 0.160592 0.392177 0.409 0.68218
ORdate_year2007 0.144387 0.392839 0.368 0.71321
ORdate_year2008 -0.061603 0.407944 -0.151 0.87997
ORdate_year2009 0.228323 0.401831 0.568 0.56989
ORdate_year2010 0.254201 0.403568 0.630 0.52877
ORdate_year2011 0.136443 0.392806 0.347 0.72833
ORdate_year2012 -0.836298 0.411427 -2.033 0.04208 *
ORdate_year2013 1.243013 1.277281 0.973 0.33047
Hypertension.compositeyes 0.163098 0.214899 0.759 0.44788
DiabetesStatusDiabetes 0.001762 0.173109 0.010 0.99188
SmokerStatusEx-smoker -0.042543 0.166115 -0.256 0.79787
SmokerStatusNever smoked -0.475594 0.224987 -2.114 0.03453 *
Med.Statin.LLDyes -0.005530 0.174239 -0.032 0.97468
Med.all.antiplateletyes -0.102128 0.239634 -0.426 0.66997
GFR_MDRD 0.003703 0.003826 0.968 0.33305
BMI -0.002562 0.020160 -0.127 0.89888
MedHx_CVDyes -0.080076 0.149648 -0.535 0.59258
stenose50-70% 0.331464 0.890049 0.372 0.70959
stenose70-90% 0.554524 0.851998 0.651 0.51514
stenose90-99% 0.875834 0.852861 1.027 0.30445
stenose100% (Occlusion) 0.464840 1.112815 0.418 0.67615
stenose50-99% 15.272495 426.600637 0.036 0.97144
stenose70-99% 0.412295 1.301410 0.317 0.75139
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1260.0 on 1022 degrees of freedom
Residual deviance: 1197.1 on 993 degrees of freedom
AIC: 1257.1
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: SMC_binned
Effect size...............: -0.144893
Standard error............: 0.078452
Odds ratio (effect size)..: 0.865
Lower 95% CI..............: 0.742
Upper 95% CI..............: 1.009
Z-value...................: -1.846907
P-value...................: 0.06476057
Hosmer and Lemeshow r^2...: 0.049918
Cox and Snell r^2.........: 0.059631
Nagelkerke's pseudo r^2...: 0.084201
Sample size of AE DB......: 2423
Sample size of model......: 1023
Missing data %............: 57.77961
Analysis of MCP1_pg_ml_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-2.00731 -0.34711 0.02959 0.15194 0.22937 0.64769 0.76029
ORdate_year2007 ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013
0.24856 0.24321 -0.04629 -0.97585 -1.27885 -0.82356 -1.34927
Hypertension.compositeyes DiabetesStatusDiabetes SmokerStatusEx-smoker SmokerStatusNever smoked
0.30340 -0.24759 -0.47356 -0.61514
Degrees of Freedom: 1025 Total (i.e. Null); 1008 Residual
Null Deviance: 1420
Residual Deviance: 1263 AIC: 1299
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9496 -1.0507 -0.4931 1.0349 2.2340
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.503198 1.420235 -1.763 0.07798 .
currentDF[, PROTEIN] -0.353613 0.080219 -4.408 1.04e-05 ***
Age 0.030642 0.008829 3.470 0.00052 ***
Gendermale -0.035905 0.154962 -0.232 0.81677
ORdate_year2003 0.176296 0.354018 0.498 0.61849
ORdate_year2004 0.277788 0.342974 0.810 0.41798
ORdate_year2005 0.686497 0.357697 1.919 0.05496 .
ORdate_year2006 0.803570 0.358069 2.244 0.02482 *
ORdate_year2007 0.316738 0.359751 0.880 0.37862
ORdate_year2008 0.307305 0.381885 0.805 0.42099
ORdate_year2009 0.092656 0.367377 0.252 0.80088
ORdate_year2010 -0.896614 0.392181 -2.286 0.02224 *
ORdate_year2011 -1.205080 0.398392 -3.025 0.00249 **
ORdate_year2012 -0.632278 0.409643 -1.543 0.12271
ORdate_year2013 -1.338528 1.264850 -1.058 0.28994
Hypertension.compositeyes 0.293106 0.210378 1.393 0.16355
DiabetesStatusDiabetes -0.261555 0.168888 -1.549 0.12146
SmokerStatusEx-smoker -0.469609 0.159516 -2.944 0.00324 **
SmokerStatusNever smoked -0.633393 0.225616 -2.807 0.00499 **
Med.Statin.LLDyes -0.081231 0.168736 -0.481 0.63023
Med.all.antiplateletyes 0.001011 0.236117 0.004 0.99658
GFR_MDRD 0.001210 0.003728 0.325 0.74541
BMI 0.025796 0.019273 1.338 0.18075
MedHx_CVDyes 0.024238 0.143615 0.169 0.86598
stenose50-70% -0.788886 1.037312 -0.761 0.44695
stenose70-90% -0.339225 0.997322 -0.340 0.73375
stenose90-99% -0.258801 0.997607 -0.259 0.79531
stenose100% (Occlusion) 0.903807 1.333659 0.678 0.49797
stenose50-99% -14.159116 431.620048 -0.033 0.97383
stenose70-99% -0.245170 1.396941 -0.176 0.86068
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1420.3 on 1025 degrees of freedom
Residual deviance: 1252.5 on 996 degrees of freedom
AIC: 1312.5
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.353613
Standard error............: 0.080219
Odds ratio (effect size)..: 0.702
Lower 95% CI..............: 0.6
Upper 95% CI..............: 0.822
Z-value...................: -4.408074
P-value...................: 1.04294e-05
Hosmer and Lemeshow r^2...: 0.118112
Cox and Snell r^2.........: 0.150833
Nagelkerke's pseudo r^2...: 0.201246
Sample size of AE DB......: 2423
Sample size of model......: 1026
Missing data %............: 57.6558
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
ORdate_year + SmokerStatus + BMI + MedHx_CVD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 ORdate_year2007
0.28411 -0.27137 -0.47462 -0.07004 1.21394 1.10756 -0.26437
ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013 SmokerStatusEx-smoker
0.66835 -0.19865 0.42551 0.20432 0.58654 -0.34571 -0.40671
SmokerStatusNever smoked BMI MedHx_CVDyes
-0.74147 0.03931 0.26866
Degrees of Freedom: 1026 Total (i.e. Null); 1010 Residual
Null Deviance: 1049
Residual Deviance: 981.2 AIC: 1015
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4223 0.3148 0.5568 0.7329 1.2886
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.346e+01 8.941e+02 0.015 0.987994
currentDF[, PROTEIN] -2.709e-01 9.387e-02 -2.886 0.003900 **
Age 1.522e-02 1.022e-02 1.490 0.136212
Gendermale 2.600e-03 1.839e-01 0.014 0.988722
ORdate_year2003 -3.731e-01 4.061e-01 -0.919 0.358208
ORdate_year2004 -4.519e-02 4.067e-01 -0.111 0.911525
ORdate_year2005 1.249e+00 4.938e-01 2.530 0.011408 *
ORdate_year2006 1.150e+00 4.803e-01 2.394 0.016646 *
ORdate_year2007 -2.685e-01 4.127e-01 -0.651 0.515268
ORdate_year2008 6.384e-01 4.741e-01 1.347 0.178106
ORdate_year2009 -2.388e-01 4.180e-01 -0.571 0.567807
ORdate_year2010 3.961e-01 4.456e-01 0.889 0.374025
ORdate_year2011 1.280e-01 4.253e-01 0.301 0.763475
ORdate_year2012 4.443e-01 4.765e-01 0.932 0.351141
ORdate_year2013 -7.022e-01 1.119e+00 -0.628 0.530263
Hypertension.compositeyes 2.688e-01 2.356e-01 1.141 0.253960
DiabetesStatusDiabetes 6.480e-02 2.031e-01 0.319 0.749615
SmokerStatusEx-smoker -4.942e-01 1.955e-01 -2.527 0.011496 *
SmokerStatusNever smoked -8.856e-01 2.598e-01 -3.409 0.000652 ***
Med.Statin.LLDyes -2.005e-02 1.979e-01 -0.101 0.919277
Med.all.antiplateletyes 2.859e-01 2.673e-01 1.070 0.284828
GFR_MDRD 3.901e-03 4.410e-03 0.885 0.376390
BMI 4.190e-02 2.422e-02 1.731 0.083540 .
MedHx_CVDyes 2.465e-01 1.682e-01 1.465 0.142856
stenose50-70% -1.457e+01 8.941e+02 -0.016 0.986996
stenose70-90% -1.495e+01 8.941e+02 -0.017 0.986661
stenose90-99% -1.507e+01 8.941e+02 -0.017 0.986549
stenose100% (Occlusion) 2.306e-01 1.169e+03 0.000 0.999843
stenose50-99% -1.542e-01 1.475e+03 0.000 0.999917
stenose70-99% -1.416e+01 8.941e+02 -0.016 0.987363
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1048.55 on 1026 degrees of freedom
Residual deviance: 966.49 on 997 degrees of freedom
AIC: 1026.5
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.270913
Standard error............: 0.093868
Odds ratio (effect size)..: 0.763
Lower 95% CI..............: 0.635
Upper 95% CI..............: 0.917
Z-value...................: -2.886115
P-value...................: 0.003900291
Hosmer and Lemeshow r^2...: 0.078264
Cox and Snell r^2.........: 0.076797
Nagelkerke's pseudo r^2...: 0.120041
Sample size of AE DB......: 2423
Sample size of model......: 1027
Missing data %............: 57.61453
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + SmokerStatus, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005
-0.30325 0.54611 0.01399 0.86746 0.79551 0.97212 0.74376
ORdate_year2006 ORdate_year2007 ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012
0.47948 0.15137 -0.91676 -0.61732 -0.37096 -1.28289 -1.46290
ORdate_year2013 SmokerStatusEx-smoker SmokerStatusNever smoked
-0.73412 -0.30615 0.29188
Degrees of Freedom: 1026 Total (i.e. Null); 1010 Residual
Null Deviance: 1209
Residual Deviance: 1065 AIC: 1099
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6863 -0.8607 0.5570 0.7794 1.7553
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 13.662173 565.741863 0.024 0.98073
currentDF[, PROTEIN] 0.542203 0.091239 5.943 2.80e-09 ***
Age 0.016602 0.009506 1.747 0.08072 .
Gendermale 0.877390 0.167288 5.245 1.56e-07 ***
ORdate_year2003 0.829016 0.438106 1.892 0.05845 .
ORdate_year2004 1.012328 0.417189 2.427 0.01524 *
ORdate_year2005 0.753614 0.420585 1.792 0.07316 .
ORdate_year2006 0.507971 0.422371 1.203 0.22911
ORdate_year2007 0.197488 0.423177 0.467 0.64073
ORdate_year2008 -0.905962 0.427284 -2.120 0.03398 *
ORdate_year2009 -0.626258 0.416034 -1.505 0.13225
ORdate_year2010 -0.374918 0.424362 -0.883 0.37697
ORdate_year2011 -1.311929 0.409831 -3.201 0.00137 **
ORdate_year2012 -1.390047 0.436994 -3.181 0.00147 **
ORdate_year2013 -0.371440 1.164790 -0.319 0.74981
Hypertension.compositeyes -0.016311 0.235812 -0.069 0.94485
DiabetesStatusDiabetes -0.259691 0.185437 -1.400 0.16139
SmokerStatusEx-smoker -0.324649 0.177549 -1.829 0.06747 .
SmokerStatusNever smoked 0.273727 0.262630 1.042 0.29729
Med.Statin.LLDyes -0.076315 0.195538 -0.390 0.69633
Med.all.antiplateletyes 0.112168 0.266958 0.420 0.67436
GFR_MDRD 0.001810 0.004079 0.444 0.65724
BMI 0.005916 0.020883 0.283 0.77694
MedHx_CVDyes 0.110460 0.161129 0.686 0.49301
stenose50-70% -14.283003 565.740874 -0.025 0.97986
stenose70-90% -14.452330 565.740802 -0.026 0.97962
stenose90-99% -14.521588 565.740806 -0.026 0.97952
stenose100% (Occlusion) -14.999461 565.741282 -0.027 0.97885
stenose50-99% -15.781913 565.742065 -0.028 0.97775
stenose70-99% -15.204332 565.741635 -0.027 0.97856
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1209.2 on 1026 degrees of freedom
Residual deviance: 1056.0 on 997 degrees of freedom
AIC: 1116
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.542203
Standard error............: 0.091239
Odds ratio (effect size)..: 1.72
Lower 95% CI..............: 1.438
Upper 95% CI..............: 2.057
Z-value...................: 5.942652
P-value...................: 2.804479e-09
Hosmer and Lemeshow r^2...: 0.126731
Cox and Snell r^2.........: 0.138617
Nagelkerke's pseudo r^2...: 0.200334
Sample size of AE DB......: 2423
Sample size of model......: 1027
Missing data %............: 57.61453
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + ORdate_year + BMI + MedHx_CVD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 ORdate_year2007
-0.27277 0.25210 0.47778 -0.03586 0.21890 0.26139 -0.55175 -0.63763
ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013 BMI MedHx_CVDyes
-0.73716 -1.28079 -1.44115 -1.67550 -1.26029 -0.67318 0.02918 0.41322
Degrees of Freedom: 1024 Total (i.e. Null); 1009 Residual
Null Deviance: 1371
Residual Deviance: 1265 AIC: 1297
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1100 -1.1200 0.6753 0.9475 1.7770
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.487068 1.372237 -0.355 0.722631
currentDF[, PROTEIN] 0.248465 0.079915 3.109 0.001876 **
Age 0.009945 0.008588 1.158 0.246839
Gendermale 0.514651 0.153628 3.350 0.000808 ***
ORdate_year2003 -0.041236 0.401630 -0.103 0.918224
ORdate_year2004 0.238300 0.391193 0.609 0.542417
ORdate_year2005 0.293659 0.401273 0.732 0.464279
ORdate_year2006 -0.529262 0.388860 -1.361 0.173495
ORdate_year2007 -0.591576 0.394251 -1.501 0.133483
ORdate_year2008 -0.695882 0.415290 -1.676 0.093806 .
ORdate_year2009 -1.249195 0.399905 -3.124 0.001786 **
ORdate_year2010 -1.421022 0.404147 -3.516 0.000438 ***
ORdate_year2011 -1.657709 0.399897 -4.145 3.39e-05 ***
ORdate_year2012 -1.371832 0.425027 -3.228 0.001248 **
ORdate_year2013 -1.508531 1.083271 -1.393 0.163750
Hypertension.compositeyes -0.118009 0.209771 -0.563 0.573736
DiabetesStatusDiabetes -0.125879 0.167896 -0.750 0.453412
SmokerStatusEx-smoker -0.097052 0.160028 -0.606 0.544202
SmokerStatusNever smoked -0.182810 0.221749 -0.824 0.409711
Med.Statin.LLDyes -0.081366 0.172008 -0.473 0.636186
Med.all.antiplateletyes 0.105192 0.236668 0.444 0.656702
GFR_MDRD -0.003066 0.003675 -0.834 0.404224
BMI 0.036420 0.019225 1.894 0.058172 .
MedHx_CVDyes 0.396450 0.143354 2.766 0.005683 **
stenose50-70% -0.335483 0.973065 -0.345 0.730268
stenose70-90% -0.328877 0.939587 -0.350 0.726321
stenose90-99% -0.250902 0.940488 -0.267 0.789640
stenose100% (Occlusion) -0.713716 1.162624 -0.614 0.539292
stenose50-99% -0.102693 1.397573 -0.073 0.941425
stenose70-99% 1.689652 1.526120 1.107 0.268227
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1371.2 on 1024 degrees of freedom
Residual deviance: 1255.6 on 995 degrees of freedom
AIC: 1315.6
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IPH
Effect size...............: 0.248465
Standard error............: 0.079915
Odds ratio (effect size)..: 1.282
Lower 95% CI..............: 1.096
Upper 95% CI..............: 1.499
Z-value...................: 3.109115
P-value...................: 0.001876488
Hosmer and Lemeshow r^2...: 0.084309
Cox and Snell r^2.........: 0.106654
Nagelkerke's pseudo r^2...: 0.144605
Sample size of AE DB......: 2423
Sample size of model......: 1025
Missing data %............: 57.69707
- processing MAC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + ORdate_year + Med.Statin.LLD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 ORdate_year2007
-0.84179 0.36943 0.46117 0.16803 0.76540 1.32673 1.29370 0.91271
ORdate_year2008 ORdate_year2009 ORdate_year2010 ORdate_year2011 ORdate_year2012 ORdate_year2013 Med.Statin.LLDyes
-0.40427 0.25255 -0.08189 -0.60610 -1.26675 0.06868 0.31061
Degrees of Freedom: 1022 Total (i.e. Null); 1008 Residual
Null Deviance: 1417
Residual Deviance: 1289 AIC: 1319
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0666 -1.0636 0.5928 1.0318 2.1302
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.328909 1.374772 0.967 0.333724
currentDF[, PROTEIN] 0.375787 0.079558 4.723 2.32e-06 ***
Age -0.013313 0.008525 -1.562 0.118370
Gendermale 0.503644 0.154091 3.268 0.001081 **
ORdate_year2003 0.181745 0.366530 0.496 0.619999
ORdate_year2004 0.769392 0.354034 2.173 0.029764 *
ORdate_year2005 1.343134 0.372096 3.610 0.000307 ***
ORdate_year2006 1.293126 0.374260 3.455 0.000550 ***
ORdate_year2007 0.906344 0.373576 2.426 0.015261 *
ORdate_year2008 -0.428300 0.394300 -1.086 0.277377
ORdate_year2009 0.205306 0.375452 0.547 0.584500
ORdate_year2010 -0.076314 0.377175 -0.202 0.839658
ORdate_year2011 -0.641886 0.377120 -1.702 0.088741 .
ORdate_year2012 -1.555668 0.454236 -3.425 0.000615 ***
ORdate_year2013 -0.975692 1.066610 -0.915 0.360318
Hypertension.compositeyes 0.010992 0.209760 0.052 0.958207
DiabetesStatusDiabetes -0.130904 0.166790 -0.785 0.432544
SmokerStatusEx-smoker 0.094923 0.157283 0.604 0.546164
SmokerStatusNever smoked 0.440592 0.224381 1.964 0.049577 *
Med.Statin.LLDyes 0.304955 0.167475 1.821 0.068622 .
Med.all.antiplateletyes -0.360437 0.238542 -1.511 0.130789
GFR_MDRD -0.001024 0.003638 -0.281 0.778414
BMI -0.015159 0.019053 -0.796 0.426245
MedHx_CVDyes 0.196355 0.143486 1.368 0.171167
stenose50-70% -0.559371 0.991250 -0.564 0.572544
stenose70-90% -0.665570 0.957841 -0.695 0.487140
stenose90-99% -0.727696 0.958343 -0.759 0.447657
stenose100% (Occlusion) -1.664013 1.215835 -1.369 0.171119
stenose50-99% 1.011372 1.430212 0.707 0.479474
stenose70-99% 1.167517 1.325550 0.881 0.378437
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1417.5 on 1022 degrees of freedom
Residual deviance: 1271.1 on 993 degrees of freedom
AIC: 1331.1
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MAC_binned
Effect size...............: 0.375787
Standard error............: 0.079558
Odds ratio (effect size)..: 1.456
Lower 95% CI..............: 1.246
Upper 95% CI..............: 1.702
Z-value...................: 4.723433
P-value...................: 2.318968e-06
Hosmer and Lemeshow r^2...: 0.103252
Cox and Snell r^2.........: 0.133303
Nagelkerke's pseudo r^2...: 0.177779
Sample size of AE DB......: 2423
Sample size of model......: 1023
Missing data %............: 57.77961
- processing SMC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + SmokerStatus, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale SmokerStatusEx-smoker SmokerStatusNever smoked
2.86041 -0.28454 -0.02413 -0.37184 -0.06239 -0.42455
Degrees of Freedom: 1022 Total (i.e. Null); 1017 Residual
Null Deviance: 1260
Residual Deviance: 1218 AIC: 1230
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0914 -1.2451 0.6989 0.8683 1.7424
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.059e+00 1.363e+00 1.511 0.130824
currentDF[, PROTEIN] -2.984e-01 8.324e-02 -3.584 0.000338 ***
Age -2.396e-02 9.176e-03 -2.612 0.009014 **
Gendermale -3.974e-01 1.669e-01 -2.381 0.017254 *
ORdate_year2003 -2.876e-01 3.871e-01 -0.743 0.457508
ORdate_year2004 -9.962e-02 3.781e-01 -0.263 0.792185
ORdate_year2005 5.046e-02 3.903e-01 0.129 0.897132
ORdate_year2006 1.940e-01 3.912e-01 0.496 0.619892
ORdate_year2007 1.522e-01 3.939e-01 0.386 0.699245
ORdate_year2008 2.802e-02 4.106e-01 0.068 0.945589
ORdate_year2009 3.387e-01 4.051e-01 0.836 0.403089
ORdate_year2010 3.372e-01 4.066e-01 0.829 0.406954
ORdate_year2011 2.708e-01 3.970e-01 0.682 0.495164
ORdate_year2012 -7.347e-01 4.145e-01 -1.773 0.076274 .
ORdate_year2013 1.324e+00 1.278e+00 1.036 0.300236
Hypertension.compositeyes 1.549e-01 2.155e-01 0.719 0.472225
DiabetesStatusDiabetes -4.933e-04 1.740e-01 -0.003 0.997738
SmokerStatusEx-smoker -5.171e-02 1.671e-01 -0.310 0.756936
SmokerStatusNever smoked -4.761e-01 2.262e-01 -2.105 0.035281 *
Med.Statin.LLDyes -2.706e-02 1.750e-01 -0.155 0.877085
Med.all.antiplateletyes -9.172e-02 2.410e-01 -0.381 0.703469
GFR_MDRD 3.902e-03 3.845e-03 1.015 0.310178
BMI -3.310e-03 2.037e-02 -0.162 0.870933
MedHx_CVDyes -8.661e-02 1.504e-01 -0.576 0.564713
stenose50-70% 2.393e-01 8.956e-01 0.267 0.789318
stenose70-90% 4.914e-01 8.567e-01 0.574 0.566251
stenose90-99% 8.067e-01 8.574e-01 0.941 0.346787
stenose100% (Occlusion) 3.784e-01 1.119e+00 0.338 0.735351
stenose50-99% 1.515e+01 4.272e+02 0.035 0.971717
stenose70-99% 3.624e-01 1.311e+00 0.276 0.782188
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1260.0 on 1022 degrees of freedom
Residual deviance: 1187.4 on 993 degrees of freedom
AIC: 1247.4
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: SMC_binned
Effect size...............: -0.29836
Standard error............: 0.083244
Odds ratio (effect size)..: 0.742
Lower 95% CI..............: 0.63
Upper 95% CI..............: 0.874
Z-value...................: -3.584146
P-value...................: 0.0003381831
Hosmer and Lemeshow r^2...: 0.057599
Cox and Snell r^2.........: 0.068484
Nagelkerke's pseudo r^2...: 0.096703
Sample size of AE DB......: 2423
Sample size of model......: 1023
Missing data %............: 57.77961
Analysis of MCP1_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ ORdate_year + DiabetesStatus +
GFR_MDRD + MedHx_CVD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 DiabetesStatusDiabetes GFR_MDRD MedHx_CVDyes
0.914168 0.226784 0.404666 0.797527 0.996647 -0.455937 -0.009503 -0.369454
Degrees of Freedom: 497 Total (i.e. Null); 490 Residual
Null Deviance: 675.4
Residual Deviance: 656.5 AIC: 672.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7837 -1.2045 0.8077 1.0189 1.6189
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.344404 1.848362 -0.727 0.4670
currentDF[, PROTEIN] -0.101647 0.098078 -1.036 0.3000
Age 0.001412 0.012458 0.113 0.9097
Gendermale -0.059074 0.218708 -0.270 0.7871
ORdate_year2003 0.270553 0.319758 0.846 0.3975
ORdate_year2004 0.414039 0.313676 1.320 0.1868
ORdate_year2005 0.814316 0.325700 2.500 0.0124 *
ORdate_year2006 1.051187 0.572596 1.836 0.0664 .
Hypertension.compositeyes 0.397265 0.280815 1.415 0.1572
DiabetesStatusDiabetes -0.531187 0.239984 -2.213 0.0269 *
SmokerStatusEx-smoker -0.191276 0.213771 -0.895 0.3709
SmokerStatusNever smoked -0.092939 0.321273 -0.289 0.7724
Med.Statin.LLDyes -0.189320 0.223037 -0.849 0.3960
Med.all.antiplateletyes 0.286712 0.341831 0.839 0.4016
GFR_MDRD -0.009395 0.005429 -1.731 0.0835 .
BMI 0.011038 0.025794 0.428 0.6687
MedHx_CVDyes -0.339943 0.202341 -1.680 0.0929 .
stenose50-70% 1.466435 1.353371 1.084 0.2786
stenose70-90% 1.693120 1.260905 1.343 0.1793
stenose90-99% 1.428644 1.256958 1.137 0.2557
stenose100% (Occlusion) 1.677634 1.607777 1.043 0.2967
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 675.45 on 497 degrees of freedom
Residual deviance: 648.01 on 477 degrees of freedom
AIC: 690.01
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.101647
Standard error............: 0.098078
Odds ratio (effect size)..: 0.903
Lower 95% CI..............: 0.745
Upper 95% CI..............: 1.095
Z-value...................: -1.036385
P-value...................: 0.3000228
Hosmer and Lemeshow r^2...: 0.040626
Cox and Snell r^2.........: 0.053611
Nagelkerke's pseudo r^2...: 0.072214
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
ORdate_year + SmokerStatus + Med.all.antiplatelet, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 SmokerStatusEx-smoker
1.06646 -0.52953 -0.32954 -0.08014 1.36511 0.89928 -0.61228
SmokerStatusNever smoked Med.all.antiplateletyes
-0.97955 0.78876
Degrees of Freedom: 495 Total (i.e. Null); 487 Residual
Null Deviance: 493.1
Residual Deviance: 434.8 AIC: 452.8
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4211 0.2345 0.4547 0.6917 1.5668
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 15.348537 782.511165 0.020 0.98435
currentDF[, PROTEIN] -0.529002 0.131880 -4.011 6.04e-05 ***
Age -0.007398 0.016528 -0.448 0.65443
Gendermale -0.093877 0.291495 -0.322 0.74741
ORdate_year2003 -0.263136 0.374993 -0.702 0.48286
ORdate_year2004 -0.126679 0.375600 -0.337 0.73591
ORdate_year2005 1.356015 0.463966 2.923 0.00347 **
ORdate_year2006 0.954240 0.846117 1.128 0.25941
Hypertension.compositeyes 0.233145 0.354307 0.658 0.51052
DiabetesStatusDiabetes 0.167424 0.327745 0.511 0.60947
SmokerStatusEx-smoker -0.611973 0.290369 -2.108 0.03507 *
SmokerStatusNever smoked -1.015844 0.400583 -2.536 0.01122 *
Med.Statin.LLDyes -0.045057 0.278620 -0.162 0.87153
Med.all.antiplateletyes 0.882001 0.418227 2.109 0.03495 *
GFR_MDRD -0.005131 0.007387 -0.695 0.48728
BMI -0.004816 0.035516 -0.136 0.89214
MedHx_CVDyes 0.002746 0.262697 0.010 0.99166
stenose50-70% -12.150316 782.509873 -0.016 0.98761
stenose70-90% -13.294854 782.509160 -0.017 0.98644
stenose90-99% -13.724431 782.509140 -0.018 0.98601
stenose100% (Occlusion) -12.845130 782.510173 -0.016 0.98690
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 493.05 on 495 degrees of freedom
Residual deviance: 427.11 on 475 degrees of freedom
AIC: 469.11
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.529002
Standard error............: 0.13188
Odds ratio (effect size)..: 0.589
Lower 95% CI..............: 0.455
Upper 95% CI..............: 0.763
Z-value...................: -4.011244
P-value...................: 6.039972e-05
Hosmer and Lemeshow r^2...: 0.133742
Cox and Snell r^2.........: 0.124489
Nagelkerke's pseudo r^2...: 0.197624
Sample size of AE DB......: 2423
Sample size of model......: 496
Missing data %............: 79.52951
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Hypertension.composite + SmokerStatus, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Hypertension.compositeyes SmokerStatusEx-smoker SmokerStatusNever smoked
0.8092 0.6602 0.6928 0.6592 -0.6083 0.1413
Degrees of Freedom: 497 Total (i.e. Null); 492 Residual
Null Deviance: 491.1
Residual Deviance: 444.9 AIC: 456.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6379 0.2873 0.4882 0.6781 1.7615
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 12.447356 835.735777 0.015 0.9881
currentDF[, PROTEIN] 0.706499 0.134553 5.251 1.52e-07 ***
Age 0.002853 0.016004 0.178 0.8585
Gendermale 0.637657 0.267028 2.388 0.0169 *
ORdate_year2003 0.549864 0.414524 1.326 0.1847
ORdate_year2004 0.805015 0.406543 1.980 0.0477 *
ORdate_year2005 0.624538 0.406253 1.537 0.1242
ORdate_year2006 0.802238 0.666470 1.204 0.2287
Hypertension.compositeyes 0.667020 0.346159 1.927 0.0540 .
DiabetesStatusDiabetes -0.314616 0.300156 -1.048 0.2946
SmokerStatusEx-smoker -0.647084 0.280760 -2.305 0.0212 *
SmokerStatusNever smoked 0.040523 0.456761 0.089 0.9293
Med.Statin.LLDyes -0.233163 0.297784 -0.783 0.4336
Med.all.antiplateletyes 0.250904 0.415699 0.604 0.5461
GFR_MDRD 0.002218 0.007104 0.312 0.7549
BMI 0.035973 0.032986 1.091 0.2755
MedHx_CVDyes 0.130183 0.256011 0.509 0.6111
stenose50-70% -14.462542 835.734125 -0.017 0.9862
stenose70-90% -13.264399 835.733950 -0.016 0.9873
stenose90-99% -13.636312 835.733939 -0.016 0.9870
stenose100% (Occlusion) -12.927065 835.734827 -0.015 0.9877
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 491.11 on 497 degrees of freedom
Residual deviance: 433.35 on 477 degrees of freedom
AIC: 475.35
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.706499
Standard error............: 0.134553
Odds ratio (effect size)..: 2.027
Lower 95% CI..............: 1.557
Upper 95% CI..............: 2.639
Z-value...................: 5.250692
P-value...................: 1.515291e-07
Hosmer and Lemeshow r^2...: 0.117609
Cox and Snell r^2.........: 0.109508
Nagelkerke's pseudo r^2...: 0.174656
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender +
DiabetesStatus + BMI + MedHx_CVD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale DiabetesStatusDiabetes BMI MedHx_CVDyes
-1.99101 0.01776 0.74177 -0.50330 0.05039 0.34743
Degrees of Freedom: 497 Total (i.e. Null); 492 Residual
Null Deviance: 552.3
Residual Deviance: 530.6 AIC: 542.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0961 0.4591 0.6214 0.7611 1.4201
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.625455 2.002948 -1.311 0.189927
currentDF[, PROTEIN] 0.080961 0.112767 0.718 0.472791
Age 0.012935 0.014179 0.912 0.361625
Gendermale 0.780891 0.236687 3.299 0.000969 ***
ORdate_year2003 -0.048044 0.377101 -0.127 0.898621
ORdate_year2004 -0.007620 0.370292 -0.021 0.983583
ORdate_year2005 0.121943 0.378812 0.322 0.747521
ORdate_year2006 -0.274026 0.609536 -0.450 0.653024
Hypertension.compositeyes 0.220572 0.312364 0.706 0.480103
DiabetesStatusDiabetes -0.517734 0.264696 -1.956 0.050470 .
SmokerStatusEx-smoker -0.074582 0.247534 -0.301 0.763187
SmokerStatusNever smoked 0.045087 0.367837 0.123 0.902445
Med.Statin.LLDyes -0.074341 0.261600 -0.284 0.776274
Med.all.antiplateletyes -0.101923 0.399828 -0.255 0.798788
GFR_MDRD -0.005948 0.006301 -0.944 0.345180
BMI 0.050533 0.029404 1.719 0.085696 .
MedHx_CVDyes 0.332431 0.225488 1.474 0.140408
stenose50-70% 1.348602 1.383495 0.975 0.329670
stenose70-90% 1.233226 1.267524 0.973 0.330583
stenose90-99% 1.441676 1.265778 1.139 0.254718
stenose100% (Occlusion) 1.539495 1.729426 0.890 0.373371
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 552.26 on 497 degrees of freedom
Residual deviance: 525.78 on 477 degrees of freedom
AIC: 567.78
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IPH
Effect size...............: 0.080961
Standard error............: 0.112767
Odds ratio (effect size)..: 1.084
Lower 95% CI..............: 0.869
Upper 95% CI..............: 1.353
Z-value...................: 0.717946
P-value...................: 0.4727908
Hosmer and Lemeshow r^2...: 0.047961
Cox and Snell r^2.........: 0.051798
Nagelkerke's pseudo r^2...: 0.077298
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing MAC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + ORdate_year + Med.Statin.LLD + GFR_MDRD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes
-0.184908 0.398880 0.308827 0.117636 0.511102 0.914874 2.042222 0.503613
GFR_MDRD
-0.008279
Degrees of Freedom: 493 Total (i.e. Null); 485 Residual
Null Deviance: 671.2
Residual Deviance: 628.1 AIC: 646.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0452 -1.1496 0.7236 1.0055 1.5749
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 14.724782 507.308344 0.029 0.976844
currentDF[, PROTEIN] 0.382779 0.102373 3.739 0.000185 ***
Age -0.017855 0.012743 -1.401 0.161175
Gendermale 0.333596 0.220692 1.512 0.130639
ORdate_year2003 0.139752 0.333565 0.419 0.675241
ORdate_year2004 0.534644 0.327507 1.632 0.102582
ORdate_year2005 0.907301 0.337597 2.688 0.007198 **
ORdate_year2006 2.119729 0.705152 3.006 0.002647 **
Hypertension.compositeyes 0.056867 0.290769 0.196 0.844943
DiabetesStatusDiabetes -0.136186 0.247553 -0.550 0.582231
SmokerStatusEx-smoker 0.037924 0.219288 0.173 0.862697
SmokerStatusNever smoked 0.204708 0.326640 0.627 0.530850
Med.Statin.LLDyes 0.416464 0.224076 1.859 0.063086 .
Med.all.antiplateletyes -0.111359 0.352671 -0.316 0.752185
GFR_MDRD -0.009851 0.005585 -1.764 0.077796 .
BMI -0.004712 0.025759 -0.183 0.854856
MedHx_CVDyes 0.136275 0.204765 0.666 0.505718
stenose50-70% -13.625344 507.306732 -0.027 0.978573
stenose70-90% -13.318413 507.306494 -0.026 0.979055
stenose90-99% -13.614962 507.306482 -0.027 0.978589
stenose100% (Occlusion) -13.983151 507.307389 -0.028 0.978010
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 671.15 on 493 degrees of freedom
Residual deviance: 620.90 on 473 degrees of freedom
AIC: 662.9
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MAC_binned
Effect size...............: 0.382779
Standard error............: 0.102373
Odds ratio (effect size)..: 1.466
Lower 95% CI..............: 1.2
Upper 95% CI..............: 1.792
Z-value...................: 3.739066
P-value...................: 0.0001847055
Hosmer and Lemeshow r^2...: 0.074874
Cox and Snell r^2.........: 0.096722
Nagelkerke's pseudo r^2...: 0.13018
Sample size of AE DB......: 2423
Sample size of model......: 494
Missing data %............: 79.61205
- processing SMC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
3.58159 -0.46804 -0.03057 -0.73346
Degrees of Freedom: 495 Total (i.e. Null); 492 Residual
Null Deviance: 595.8
Residual Deviance: 558.3 AIC: 566.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3169 -1.1623 0.6142 0.8461 1.4675
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 19.276414 500.331949 0.039 0.96927
currentDF[, PROTEIN] -0.460853 0.112834 -4.084 4.42e-05 ***
Age -0.037892 0.014325 -2.645 0.00816 **
Gendermale -0.824754 0.267531 -3.083 0.00205 **
ORdate_year2003 -0.173591 0.353376 -0.491 0.62326
ORdate_year2004 0.107358 0.350189 0.307 0.75917
ORdate_year2005 0.244942 0.361925 0.677 0.49855
ORdate_year2006 0.642750 0.667287 0.963 0.33543
Hypertension.compositeyes -0.343329 0.336577 -1.020 0.30770
DiabetesStatusDiabetes -0.201077 0.262883 -0.765 0.44434
SmokerStatusEx-smoker 0.195008 0.239805 0.813 0.41611
SmokerStatusNever smoked -0.132340 0.339916 -0.389 0.69703
Med.Statin.LLDyes -0.180751 0.246735 -0.733 0.46382
Med.all.antiplateletyes -0.066521 0.382296 -0.174 0.86186
GFR_MDRD -0.001633 0.005981 -0.273 0.78480
BMI -0.022195 0.029332 -0.757 0.44924
MedHx_CVDyes -0.108767 0.225402 -0.483 0.62942
stenose50-70% -13.861849 500.329841 -0.028 0.97790
stenose70-90% -14.062371 500.329485 -0.028 0.97758
stenose90-99% -13.922070 500.329474 -0.028 0.97780
stenose100% (Occlusion) -14.917614 500.330473 -0.030 0.97621
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 595.82 on 495 degrees of freedom
Residual deviance: 546.97 on 475 degrees of freedom
AIC: 588.97
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: SMC_binned
Effect size...............: -0.460853
Standard error............: 0.112834
Odds ratio (effect size)..: 0.631
Lower 95% CI..............: 0.506
Upper 95% CI..............: 0.787
Z-value...................: -4.084341
P-value...................: 4.420205e-05
Hosmer and Lemeshow r^2...: 0.081996
Cox and Snell r^2.........: 0.093803
Nagelkerke's pseudo r^2...: 0.13416
Sample size of AE DB......: 2423
Sample size of model......: 496
Missing data %............: 79.52951 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
B. Cross-sectional analysis symptoms
We will perform a cross-sectional analysis between plaque and plasma MCP1, IL6, and IL6R levels and the ‘clinical status’ of the plaque in terms of presence of patients’ symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:
- stroke
- TIA
- retinal infarction
- amaurosis fugax
- asymptomatic
Model 1
In this model we correct for Age, Gender, and year of surgery.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "AsymptSympt"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
# + Hypertension.composite + DiabetesStatus + SmokerCurrent +
# Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
# CAD_history + Stroke_history + Peripheral.interv + stenose
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
Analysis of MCP1_pg_ug_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
0.2510 0.2653 0.0324 -0.4373
Degrees of Freedom: 1197 Total (i.e. Null); 1194 Residual
Null Deviance: 827
Residual Deviance: 805 AIC: 813
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5120 0.3491 0.4250 0.5151 1.0325
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.75194 0.76721 -0.980 0.32704
currentDF[, PROTEIN] 0.25662 0.10362 2.477 0.01326 *
Age 0.03124 0.01032 3.027 0.00247 **
Gendermale -0.45180 0.22013 -2.052 0.04013 *
ORdate_year2003 1.33789 0.45960 2.911 0.00360 **
ORdate_year2004 0.83105 0.39880 2.084 0.03717 *
ORdate_year2005 1.39139 0.44593 3.120 0.00181 **
ORdate_year2006 1.10178 0.42995 2.563 0.01039 *
ORdate_year2007 1.01489 0.42046 2.414 0.01579 *
ORdate_year2008 0.97459 0.46177 2.111 0.03481 *
ORdate_year2009 1.11790 0.45989 2.431 0.01507 *
ORdate_year2010 1.26611 0.50749 2.495 0.01260 *
ORdate_year2011 1.37651 0.48495 2.838 0.00453 **
ORdate_year2012 1.40208 0.50471 2.778 0.00547 **
ORdate_year2013 15.32343 519.27613 0.030 0.97646
ORdate_year2014 15.38790 2399.54474 0.006 0.99488
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 826.98 on 1197 degrees of freedom
Residual deviance: 786.30 on 1182 degrees of freedom
AIC: 818.3
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.256621
Standard error............: 0.103616
Odds ratio (effect size)..: 1.293
Lower 95% CI..............: 1.055
Upper 95% CI..............: 1.584
Z-value...................: 2.476654
P-value...................: 0.01326204
Hosmer and Lemeshow r^2...: 0.0492
Cox and Snell r^2.........: 0.033393
Nagelkerke's pseudo r^2...: 0.066976
Sample size of AE DB......: 2423
Sample size of model......: 1198
Missing data %............: 50.55716
Analysis of MCP1_pg_ml_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
0.48608 0.33793 0.03014 -0.52960
Degrees of Freedom: 1198 Total (i.e. Null); 1195 Residual
Null Deviance: 827.2
Residual Deviance: 800.7 AIC: 808.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5623 0.3401 0.4248 0.5143 1.1318
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.51811 0.77059 -0.672 0.50136
currentDF[, PROTEIN] 0.33370 0.10935 3.052 0.00228 **
Age 0.02973 0.01033 2.880 0.00398 **
Gendermale -0.54150 0.22360 -2.422 0.01545 *
ORdate_year2003 1.36327 0.46060 2.960 0.00308 **
ORdate_year2004 0.87473 0.39956 2.189 0.02858 *
ORdate_year2005 1.38021 0.44077 3.131 0.00174 **
ORdate_year2006 1.01024 0.42649 2.369 0.01785 *
ORdate_year2007 0.94587 0.41945 2.255 0.02413 *
ORdate_year2008 0.85349 0.46343 1.842 0.06552 .
ORdate_year2009 1.00036 0.46139 2.168 0.03015 *
ORdate_year2010 1.20841 0.50936 2.372 0.01767 *
ORdate_year2011 1.24140 0.48877 2.540 0.01109 *
ORdate_year2012 1.29953 0.50696 2.563 0.01037 *
ORdate_year2013 15.21333 513.09680 0.030 0.97635
ORdate_year2014 15.36424 2399.54474 0.006 0.99489
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 827.22 on 1198 degrees of freedom
Residual deviance: 783.22 on 1183 degrees of freedom
AIC: 815.22
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.333701
Standard error............: 0.10935
Odds ratio (effect size)..: 1.396
Lower 95% CI..............: 1.127
Upper 95% CI..............: 1.73
Z-value...................: 3.051694
P-value...................: 0.002275542
Hosmer and Lemeshow r^2...: 0.053185
Cox and Snell r^2.........: 0.036028
Nagelkerke's pseudo r^2...: 0.072291
Sample size of AE DB......: 2423
Sample size of model......: 1199
Missing data %............: 50.51589
Analysis of MCP1_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN],
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN]
1.726 0.282
Degrees of Freedom: 555 Total (i.e. Null); 554 Residual
Null Deviance: 479
Residual Deviance: 473.3 AIC: 477.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3978 0.4254 0.5172 0.6128 0.9755
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.11424 0.97215 0.118 0.90645
currentDF[, PROTEIN] 0.36545 0.12536 2.915 0.00356 **
Age 0.01685 0.01376 1.224 0.22100
Gendermale -0.31530 0.27652 -1.140 0.25418
ORdate_year2003 0.87346 0.39514 2.211 0.02707 *
ORdate_year2004 0.64995 0.36496 1.781 0.07494 .
ORdate_year2005 0.98125 0.38250 2.565 0.01031 *
ORdate_year2006 1.24417 0.69869 1.781 0.07496 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 478.98 on 555 degrees of freedom
Residual deviance: 463.27 on 548 degrees of freedom
AIC: 479.27
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.365453
Standard error............: 0.125363
Odds ratio (effect size)..: 1.441
Lower 95% CI..............: 1.127
Upper 95% CI..............: 1.843
Z-value...................: 2.91516
P-value...................: 0.003555061
Hosmer and Lemeshow r^2...: 0.032796
Cox and Snell r^2.........: 0.027857
Nagelkerke's pseudo r^2...: 0.048241
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Model 2
In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis..
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "AsymptSympt"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year +
Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
Analysis of MCP1_pg_ug_2015_rank.
- processing AsymptSymptglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Med.all.antiplatelet + GFR_MDRD + stenose,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale Med.all.antiplateletyes GFR_MDRD stenose50-70%
15.33048 0.27388 0.03227 -0.42700 -0.92831 0.00790 -13.42936
stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
-15.00489 -14.74684 -0.33897 -15.81472 -0.54263
Degrees of Freedom: 1037 Total (i.e. Null); 1026 Residual
Null Deviance: 726.9
Residual Deviance: 689.5 AIC: 713.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.1354 0.2804 0.4107 0.5329 1.0358
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.557e+01 1.582e+03 0.010 0.99214
currentDF[, PROTEIN] 2.315e-01 1.109e-01 2.088 0.03679 *
Age 3.308e-02 1.302e-02 2.540 0.01108 *
Gendermale -3.706e-01 2.410e-01 -1.537 0.12420
ORdate_year2003 1.331e+00 4.719e-01 2.822 0.00478 **
ORdate_year2004 8.288e-01 4.138e-01 2.003 0.04516 *
ORdate_year2005 1.418e+00 4.685e-01 3.026 0.00248 **
ORdate_year2006 1.206e+00 4.536e-01 2.659 0.00785 **
ORdate_year2007 1.308e+00 4.797e-01 2.726 0.00641 **
ORdate_year2008 1.294e+00 5.267e-01 2.457 0.01399 *
ORdate_year2009 1.123e+00 4.800e-01 2.341 0.01926 *
ORdate_year2010 1.461e+00 5.219e-01 2.798 0.00514 **
ORdate_year2011 1.744e+00 5.716e-01 3.052 0.00228 **
ORdate_year2012 1.526e+00 5.812e-01 2.626 0.00863 **
ORdate_year2013 1.536e+01 9.480e+02 0.016 0.98708
Hypertension.compositeyes -2.319e-01 3.494e-01 -0.664 0.50696
DiabetesStatusDiabetes -1.203e-01 2.466e-01 -0.488 0.62568
SmokerStatusEx-smoker -3.315e-01 2.368e-01 -1.400 0.16154
SmokerStatusNever smoked -6.123e-02 3.624e-01 -0.169 0.86582
Med.Statin.LLDyes -2.931e-01 2.738e-01 -1.071 0.28439
Med.all.antiplateletyes -8.940e-01 4.852e-01 -1.843 0.06537 .
GFR_MDRD 5.771e-03 5.581e-03 1.034 0.30116
BMI -8.490e-03 2.844e-02 -0.299 0.76531
MedHx_CVDyes 1.198e-01 2.131e-01 0.562 0.57402
stenose50-70% -1.405e+01 1.582e+03 -0.009 0.99291
stenose70-90% -1.563e+01 1.582e+03 -0.010 0.99212
stenose90-99% -1.531e+01 1.582e+03 -0.010 0.99228
stenose100% (Occlusion) -1.723e-02 2.042e+03 0.000 0.99999
stenose50-99% -1.690e+01 1.582e+03 -0.011 0.99147
stenose70-99% -2.041e+00 1.893e+03 -0.001 0.99914
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 726.94 on 1037 degrees of freedom
Residual deviance: 666.93 on 1008 degrees of freedom
AIC: 726.93
Number of Fisher Scoring iterations: 16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.231511
Standard error............: 0.110872
Odds ratio (effect size)..: 1.261
Lower 95% CI..............: 1.014
Upper 95% CI..............: 1.566
Z-value...................: 2.088098
P-value...................: 0.03678904
Hosmer and Lemeshow r^2...: 0.08256
Cox and Snell r^2.........: 0.05618
Nagelkerke's pseudo r^2...: 0.111561
Sample size of AE DB......: 2423
Sample size of model......: 1038
Missing data %............: 57.16054
Analysis of MCP1_pg_ml_2015_rank.
- processing AsymptSymptglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurred
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + Med.all.antiplatelet + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale Med.all.antiplateletyes stenose50-70% stenose70-90%
16.23590 0.39997 0.02377 -0.49817 -0.89815 -13.08924 -14.73442
stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
-14.43974 -0.06847 -15.51827 -0.39970
Degrees of Freedom: 1037 Total (i.e. Null); 1027 Residual
Null Deviance: 726.9
Residual Deviance: 684.3 AIC: 706.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.2264 0.2749 0.4051 0.5261 1.2035
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.571e+01 1.576e+03 0.010 0.99205
currentDF[, PROTEIN] 3.638e-01 1.192e-01 3.053 0.00226 **
Age 3.075e-02 1.305e-02 2.356 0.01847 *
Gendermale -4.633e-01 2.448e-01 -1.892 0.05848 .
ORdate_year2003 1.369e+00 4.741e-01 2.888 0.00388 **
ORdate_year2004 9.018e-01 4.154e-01 2.171 0.02995 *
ORdate_year2005 1.448e+00 4.656e-01 3.109 0.00188 **
ORdate_year2006 1.134e+00 4.516e-01 2.512 0.01200 *
ORdate_year2007 1.266e+00 4.805e-01 2.634 0.00843 **
ORdate_year2008 1.186e+00 5.298e-01 2.238 0.02519 *
ORdate_year2009 9.737e-01 4.829e-01 2.017 0.04374 *
ORdate_year2010 1.394e+00 5.259e-01 2.651 0.00803 **
ORdate_year2011 1.593e+00 5.765e-01 2.763 0.00574 **
ORdate_year2012 1.401e+00 5.842e-01 2.397 0.01652 *
ORdate_year2013 1.527e+01 9.325e+02 0.016 0.98694
Hypertension.compositeyes -2.328e-01 3.504e-01 -0.664 0.50644
DiabetesStatusDiabetes -1.165e-01 2.472e-01 -0.471 0.63746
SmokerStatusEx-smoker -3.065e-01 2.372e-01 -1.292 0.19627
SmokerStatusNever smoked -2.896e-02 3.628e-01 -0.080 0.93638
Med.Statin.LLDyes -2.757e-01 2.748e-01 -1.003 0.31563
Med.all.antiplateletyes -8.961e-01 4.841e-01 -1.851 0.06415 .
GFR_MDRD 5.896e-03 5.597e-03 1.053 0.29221
BMI -8.273e-03 2.830e-02 -0.292 0.77005
MedHx_CVDyes 1.296e-01 2.139e-01 0.606 0.54481
stenose50-70% -1.392e+01 1.576e+03 -0.009 0.99296
stenose70-90% -1.553e+01 1.576e+03 -0.010 0.99214
stenose90-99% -1.520e+01 1.576e+03 -0.010 0.99230
stenose100% (Occlusion) 7.679e-02 2.036e+03 0.000 0.99997
stenose50-99% -1.677e+01 1.576e+03 -0.011 0.99151
stenose70-99% -2.030e+00 1.882e+03 -0.001 0.99914
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 726.94 on 1037 degrees of freedom
Residual deviance: 661.78 on 1008 degrees of freedom
AIC: 721.78
Number of Fisher Scoring iterations: 16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.363828
Standard error............: 0.119158
Odds ratio (effect size)..: 1.439
Lower 95% CI..............: 1.139
Upper 95% CI..............: 1.817
Z-value...................: 3.053335
P-value...................: 0.002263135
Hosmer and Lemeshow r^2...: 0.089643
Cox and Snell r^2.........: 0.06085
Nagelkerke's pseudo r^2...: 0.120834
Sample size of AE DB......: 2423
Sample size of model......: 1038
Missing data %............: 57.16054
Analysis of MCP1_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
ORdate_year + Med.Statin.LLD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes
1.2706 0.3012 0.8671 0.6864 0.9693 1.6028 -0.4499
Degrees of Freedom: 497 Total (i.e. Null); 491 Residual
Null Deviance: 442.3
Residual Deviance: 428.4 AIC: 442.4
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4541 0.3352 0.5033 0.6515 1.1052
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.513e+01 1.362e+03 0.011 0.99114
currentDF[, PROTEIN] 3.584e-01 1.314e-01 2.728 0.00638 **
Age 2.106e-02 1.647e-02 1.279 0.20099
Gendermale -3.957e-01 3.025e-01 -1.308 0.19085
ORdate_year2003 9.329e-01 4.115e-01 2.267 0.02339 *
ORdate_year2004 7.111e-01 3.832e-01 1.856 0.06348 .
ORdate_year2005 1.028e+00 4.066e-01 2.529 0.01145 *
ORdate_year2006 1.769e+00 8.457e-01 2.092 0.03644 *
Hypertension.compositeyes -4.844e-01 4.447e-01 -1.089 0.27601
DiabetesStatusDiabetes 1.799e-01 3.266e-01 0.551 0.58173
SmokerStatusEx-smoker -1.731e-01 2.874e-01 -0.602 0.54698
SmokerStatusNever smoked -4.388e-01 4.130e-01 -1.063 0.28793
Med.Statin.LLDyes -3.813e-01 3.150e-01 -1.211 0.22603
Med.all.antiplateletyes -5.276e-01 5.178e-01 -1.019 0.30821
GFR_MDRD 9.532e-03 7.090e-03 1.344 0.17881
BMI 1.421e-02 3.461e-02 0.410 0.68152
MedHx_CVDyes 9.952e-02 2.659e-01 0.374 0.70822
stenose50-70% -1.392e+01 1.362e+03 -0.010 0.99184
stenose70-90% -1.538e+01 1.362e+03 -0.011 0.99099
stenose90-99% -1.504e+01 1.362e+03 -0.011 0.99119
stenose100% (Occlusion) -5.145e-02 1.712e+03 0.000 0.99998
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 442.26 on 497 degrees of freedom
Residual deviance: 413.78 on 477 degrees of freedom
AIC: 455.78
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.358433
Standard error............: 0.131412
Odds ratio (effect size)..: 1.431
Lower 95% CI..............: 1.106
Upper 95% CI..............: 1.852
Z-value...................: 2.727545
P-value...................: 0.006380746
Hosmer and Lemeshow r^2...: 0.064393
Cox and Snell r^2.........: 0.055582
Nagelkerke's pseudo r^2...: 0.094438
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
C. Longitudinal analysis secondary clinical outcome
For the longitudinal analyses of plaque and plasma MCP1, IL6, and IL6R levels and secondary cardiovascular events over a three-year follow-up period.
The primary outcome is defined as “a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death”, i.e. major adverse cardiovascular events (MACE). Variable: epmajor.3years, these include: - myocardial infarction (MI) - cerebral infarction (CVA/stroke) - cardiovascular death (exact cause to be investigated) - cerebral bleeding (CVA/stroke) - fatal myocardial infarction (MI) - fatal cerebral infarction - fatal cerebral bleeding - sudden death - fatal heart failure - fatal aneurysm rupture - other cardiovascular death..
The secondary outcomes will be
- incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable:
epstroke.3years, these include:- cerebral infarction (CVA/stroke)
- cerebral bleeding (CVA/stroke)
- fatal cerebral infarction
- fatal cerebral bleeding.
- incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable:
epcoronary.3years, these include:- myocardial infarction (MI)
- coronary angioplasty (PCI/PTCA)
- cardiovascular death (exact cause to be investigated)
- coronary bypass (CABG)
- fatal myocardial infarction (MI)
- sudden death.
- cardiovascular death - variable:
epcvdeath.3years, these include:- cardiovascular death (exact cause to be investigated)
- fatal myocardial infarction (MI)
- fatal cerebral infarction
- fatal cerebral bleeding
- sudden death
- fatal heart failure
- fatal aneurysm rupture
- other cardiovascular death..
30- and 90-days FU events
We will use 3-year follow-up, but we will also calculate 30 days and 90 days follow-up ‘time-to-event’ variables. On average there are 365.25 days in a year. We can calculate 30-days and 90-days follow-up time based on the three years follow-up.
cutt.off.30days = (1/365.25) * 30
cutt.off.90days = (1/365.25) * 90
# Fix maximum FU of 30 and 90 days
AEDB <- AEDB %>%
mutate(
FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
)
AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary",
"max.followup",
"FU.cutt.off.3years",
"FU.cutt.off.30days",
"FU.cutt.off.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
rm(AEDB.temp)
AEDB.CEA <- AEDB.CEA %>%
mutate(
FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
)
AEDB.CEA.temp <- subset(AEDB.CEA, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary",
"max.followup",
"FU.cutt.off.3years",
"FU.cutt.off.30days",
"FU.cutt.off.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)
DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)
rm(AEDB.CEA.temp)
Here we will calculate the new 30- and 90-days follow-up of the events and their event-times of interest:
- MACE (
epmajor.3years) - Stroke (
epstroke.3years) - Coronary events (
epcoronary.3years) - Cardiovascular death (
epcvdeath.3years)
avg_days_in_year = 365.25
cutt.off.30days.scaled <- cutt.off.30days * 365.25
cutt.off.90days.scaled <- cutt.off.90days * 365.25
# Event times
AEDB <- AEDB %>%
mutate(
ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
)
AEDB.CEA <- AEDB.CEA %>%
mutate(
ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
)
attach(AEDB)The following objects are masked from AEDB.CEA:
ABI_70, ABI_max, ABI_mean, ABI_min, ABI_OP, ablock, ablock2, ablock3, aceinhib, aceinhib2, acetylsa, Adiponectin_pg_ug_2015, AE_AAA_bijzonderheden, Age, Age_Q, AgeSQR,
aid, AlcoholUse, Aldosteron_recode, alg10201, alg10202, alg10203, alg10204, alg10205, alg105, alg106, alg109, alg110, alg113, alg114, alg115, ALOX5, analg2, analg3,
analgeti, Ang2, angioii, ANGPT2, anti_apoA1_IgG, anti_apoA1_index, anti_apoA1_na, antiall, antiall2, antiarrh, antiarrh2, ANXA2, AP_Dx, AP_Dx1, AP_Dx2, APOB, artercon,
Artery_summary, arteryop, AsymptSympt, AsymptSympt2G, bblock, bblock2, blocko, blocksnr, BMI, BMI_US, BMI_WHO, BMI30ormore, brain401, brain402, brain403, brain404,
brain405, brain406, brain407, brain408, brain409, brain410, brain411, brain412, brain413, brn40701, bspoed, CAD_Dx, CAD_Dx1, CAD_Dx2, CAD_history, CADPAOD_history,
Calc.bin, calcification, calcium, calcium2, calreg, carbasal, cardioembolic, Caspase3_7, CAV1, CD44, CD44V3, CEA_or_CAS, CEL, CFD_recalc, cholverl, cholverl2,
cholverl3, CI_history, clau1, clau2, Claudication, clopidog, CML, collagen, Collagen.bin, combi1, combi2, combi3, comorbidity.DM, concablo, concablo2, concablo3,
concace2, concacei, concacet, concalle, concanal, concanal2, concanal3, concangi, concanta2, concanti, concanti2, concbblo, concbblo2, conccalc, conccalc2, conccalreg,
conccarb, concchol, concchol2, concchol3, concclau1, concclau2, concclop, conccom1, conccom2, conccom3, conccort, conccorthorm2, concderm, concdig, concdig2, concdig3,
concdig4, concdipy, concdiur, concdiur2, concdiur3, concerec, conceye, concgluc, concgluc2, concgluc3, concgluc4, concgrel, concinsu, conciron, conciron2, concneur,
concneur2, concneur3, concneur4, concnitr, concnitr2, concotant, concotcor, concoth2, concothe, concpros, concpsy5, concren, concresp, concrheu, concrheu2, concrheu3,
concsta2, concstat, concthro, concthyr, concthyr2, concvit2, concvita, Contralateral_surgery, conwhen, corticos, cortihorm2, creat, crp_all, CRP_avg, CRP_dif,
crp_source, CRP_var, CST3_pg_ug, CST3_serum_luminex, CTGF, cTNI_plasma, CTSA, CTSB, CTSL1, CTSS, cyr61, date_ic_patient, date_ic_researcher, Date.of.birth,
date.previous.operation, date1yr, date3mon, dateapprox_latest, dateapprox_worst, dateapprox1, dateapprox2, dateapprox3, dateapprox4, dateend1, dateend2, dateend3,
dateend4, dateend5, dateend6, dateexact_latest, dateexact_worst, dateexact1, dateexact2, dateexact3, dateexact4, dateok, dermacor, DiabetesStatus, diastoli, diet801,
diet802, diet803, diet804, diet805, diet806, diet807, diet808, diet809, diet810, diet811, diet812, diet813, diet814, diet815, diet816, diet817, diet818, diet819,
diet820, diet821, diet822, diet823, diet824, dipyridi, diuretic, diuretic2, diuretic3, DM, DM.composite, duaalantiplatelet, duplend, eaindexl, eaindexr, eCigarettes,
edaplaqu_recalc, edavrspl, EGR, EMMPRIN_45kD, EMMPRIN_58kD, ENDOGLIN, endpoint1, endpoint2, endpoint3, endpoint4, endpoint5, endpoint6, Eotaxin1, EP_CAD,
ep_cad_t_30days, ep_cad_t_3years, EP_CAD_time, ep_cad.30days, EP_CI, ep_ci_t_30days, ep_ci_t_3years, EP_CI_time, ep_com_t_30days, ep_com_t_3years, EP_composite,
EP_composite_time, EP_coronary, ep_coronary_t_30days, ep_coronary_t_3years, ep_coronary_t_90days, EP_coronary_time, EP_CVdeath, ep_cvdeath_t_30days,
ep_cvdeath_t_3years, ep_cvdeath_t_90days, EP_CVdeath_time, EP_death, ep_death_t_30days, ep_death_t_3years, EP_death_time, EP_fatalCVA, ep_fatalCVA_t_30days,
ep_fatalCVA_t_3years, EP_fatalCVA_time, EP_hemorrhagic_stroke, ep_hemorrhagic_stroke_t_3years, EP_hemorrhagic_stroke_time, ep_hemorrhagic_stroke.3years,
EP_ischemic_stroke, ep_ischemic_stroke_t_3years, EP_ischemic_stroke_time, ep_ischemic_stroke.3years, EP_leg_amputation, EP_leg_amputation_time,
ep_legamputation_t_30days, ep_legamputation_t_3years, EP_major, ep_major_t_30days, ep_major_t_3years, ep_major_t_90days, EP_major_time, EP_MI, ep_mi_t_30days,
ep_mi_t_3years, EP_MI_time, EP_nonstroke_event, EP_nonstroke_event_time, ep_nonstroke_t_3years, EP_peripheral, ep_peripheral_t_30days, ep_peripheral_t_3years,
EP_peripheral_time, EP_pta, ep_pta_t_30days, ep_pta_t_3years, EP_pta_time, EP_stroke, ep_stroke_t_30days, ep_stroke_t_3years, ep_stroke_t_90days, EP_stroke_time,
EP_strokeCVdeath, ep_strokeCVdeath_t_30days, ep_strokeCVdeath_t_3years, EP_strokeCVdeath_time, EP_strokedeath, ep_strokedeath_t_30days, ep_strokedeath_t_3years,
EP_strokedeath_time, ePackYearsSmoking, epcad.3years, epci.30days, epci.3years, epcom.30days, epcom.3years, epcoronary.30days, epcoronary.3years, epcoronary.90days,
epcvdeath.30days, epcvdeath.3years, epcvdeath.90days, epdeath.30days, epdeath.3years, epfatalCVA.30days, epfatalCVA.3years, eplegamputation.30days,
eplegamputation.3years, epmajor.30days, epmajor.3years, epmajor.90days, epmi.30days, epmi.3years, epnonstroke.3years, epperipheral.30days, epperipheral.3years,
eppta.30days, eppta.3years, epstroke.30days, epstroke.3years, epstroke.90days, epstrokeCVdeath.30days, epstrokeCVdeath.3years, epstrokedeath.30days,
epstrokedeath.3years, erec, Estradiol, everstroke_composite, Everstroke_Ipsilateral, exer901, exer902, exer903, exer904, exer905, exer906, exer9071, exer9072, exer9073,
exer9074, exer9075, exer9076, exer908, exer909, exer910, eyedrop, EZis, FABP_serum, FABP4, FABP4_pg_ug, FABP4_serum_luminex, fat, Fat.bin_10, Fat.bin_40,
Femoral.interv, FH_AAA_broth, FH_AAA_comp, FH_AAA_mat, FH_AAA_parent, FH_AAA_pat, FH_AAA_sibling, FH_AAA_sis, FH_amp_broth, FH_amp_comp, FH_amp_mat, FH_amp_parent,
FH_amp_pat, FH_amp_sibling, FH_amp_sis, FH_CAD_broth, FH_CAD_comp, FH_CAD_mat, FH_CAD_parent, FH_CAD_pat, FH_CAD_sibling, FH_CAD_sis, FH_corcalc_broth, FH_corcalc_comp,
FH_corcalc_mat, FH_corcalc_parent, FH_corcalc_pat, FH_corcalc_sibling, FH_corcalc_sis, FH_CVD_broth, FH_CVD_comp, FH_CVD_mat, FH_CVD_parent, FH_CVD_pat, FH_CVD_sibling,
FH_CVD_sis, FH_CVdeath_broth, FH_CVdeath_comp, FH_CVdeath_mat, FH_CVdeath_parent, FH_CVdeath_pat, FH_CVdeath_sibling, FH_CVdeath_sis, FH_DM_broth, FH_DM_comp,
FH_DM_mat, FH_DM_parent, FH_DM_pat, FH_DM_sibling, FH_DM_sis, FH_HC_broth, FH_HC_comp, FH_HC_mat, FH_HC_parent, FH_HC_pat, FH_HC_sibling, FH_HC_sis, FH_HT_broth,
FH_HT_comp, FH_HT_mat, FH_HT_parent, FH_HT_pat, FH_HT_sibling, FH_HT_sis, FH_MI_broth, FH_MI_comp, FH_MI_mat, FH_MI_parent, FH_MI_pat, FH_MI_sibling, FH_MI_sis,
FH_otherCVD_broth, FH_otherCVD_comp, FH_otherCVD_mat, FH_otherCVD_parent, FH_otherCVD_pat, FH_otherCVD_sibling, FH_otherCVD_sis, FH_PAD_broth, FH_PAD_comp, FH_PAD_mat,
FH_PAD_parent, FH_PAD_pat, FH_PAD_sibling, FH_PAD_sis, FH_PAV_broth, FH_PAV_comp, FH_PAV_mat, FH_PAV_parent, FH_PAV_pat, FH_PAV_sibling, FH_PAV_sis, FH_POB_broth,
FH_POB_comp, FH_POB_mat, FH_POB_parent, FH_POB_pat, FH_POB_sibling, FH_POB_sis, FH_risk_broth, FH_risk_comp, FH_risk_mat, FH_risk_parent, FH_risk_pat, FH_risk_sibling,
FH_risk_sis, FH_Stroke_broth, FH_Stroke_comp, FH_Stroke_mat, FH_Stroke_parent, FH_Stroke_pat, FH_Stroke_sibling, FH_Stroke_sis, FH_tromb_broth, FH_tromb_comp,
FH_tromb_mat, FH_tromb_parent, FH_tromb_pat, FH_tromb_sibling, FH_tromb_sis, filter_$, folicaci, followup1, followup2, followup3, Fontaine, FU_check, FU_check_date,
FU.cutt.off.30days, FU.cutt.off.3years, FU.cutt.off.90days, FU1JAAR, FU2JAAR, FU3JAAR, FURIN_low, FURIN_up, GDF15_plasma, geen_med, Gender, GFR_CG, GFR_MDRD, glucose,
GR_Segment, GrB_plaque, GrB_serum, grel, GrK_plaque, GrK_serum, GrM_plaque, GrM_serum, HA, hb, HDAC9, HDL, HDL_2016, HDL_all, HDL_avg, HDL_clinic, HDL_dif, HDL_final,
HDL_finalCU, hdl_source, HDL_var, heart300, heart301, heart302, heart303, heart304, heart305, heart306, heart307, heart308, heart309, heart310, heart311, heart312,
heart313, heart314, heart315, heart316, heart317, heart318, heart319, heart320, heart321, heart322, heart323, heart324, heart325, heart326, heart327, heart328, HIF1A,
ho1, homocys, Hospital, hrt31301, hsCRP_plasma, ht, HYAL55KD, HYALURON, Hypertension.composite, Hypertension.drugs, Hypertension.selfreport,
Hypertension.selfreportdrug, Hypertension1, Hypertension2, IL1_Beta, IL10, IL12, IL13, IL17, IL2, IL21, IL4, IL5, IL6, IL6_pg_ug_2015, IL6R_pg_ug_2015, IL8,
IL8_pg_ug_2015, IL9, indexsymptoms_latest, indexsymptoms_latest_4g, indexsymptoms_worst, indexsymptoms_worst_4g, INFG, informedconsent, insulin, insuline, INVULDAT,
IP10, IPH_extended.bin, IPH.bin, ironfoli, ironfoli2, KDOQI, latest, LDL, LDL_2016, LDL_all, LDL_avg, LDL_clinic, LDL_dif, LDL_final, LDL_finalCU, ldl_source, LDL_var,
leg501, leg502, leg503, leg504, leg505, leg506, leg507, leg508, leg509, leg510, leg511, leg512, leg513, leg514, leg515, leg516, leg517, leg518, leg519, leg520,
LMW1STME, LTB4, LTB4R, macmean0, macrophages, macrophages_location, Macrophages.bin, MAP, Mast_cells_plaque, max.followup, MCP1, MCP1_pg_ug_2015, MCSF_pg_ug_2015, MDC,
Med_notes, Med.ablock, Med.ACE_inh, Med.acetylsal, Med.acetylsal_Combi1, Med.acetylsal_Combi2, Med.acetylsal_Combi3, Med.ADPinh, Med.all.antiplatelet,
Med.angiot2.antag, Med.antiarrh, Med.anticoagulants, Med.ascal, Med.aspirin.derived, Med.bblocker, Med.calc_antag, Med.dipyridamole, Med.diuretic, Med.LLD, Med.nitrate,
Med.otheranthyp, Med.renin, Med.statin, Med.statin.derived, Med.Statin.LLD, Med.statin2, MedHx_CVD, media, MG_H1, MI_Dx, MI_Dx1, MI_Dx2, MIF, MIG, MIP1a,
miRNA100_RNU19, miRNA100_RNU48, miRNA155_RNU19, miRNA155_RNU48, MMP14, MMP2, MMP2TIMP2, MMP8, MMP9, MMP9TIMP1, MPO_plasma, MRP_14, MRP_8, MRP_8_14C, MRP_8_14C_buhlmann,
MRP14_plasma, MRP8_14C_plasma, MRP8_plasma, negatibl, neuropsy, neuropsy2, neuropsy3, neuropsy4, neurpsy5, neutrophils, NGAL, NGAL_low, NGAL_MMP9_complex,
NGAL_MMP9_local, NGAL_MMP9_peripheral, NGAL_total, NGAL_up, nitrate, nitrate2, NOD1, NOD2, nogobt1_recalculated, NTproBNP_plasma, Number_Events_Sorter,
Number_Sorted_CD14, Number_Sorted_CD20, Number_Sorted_CD4_Cells, Number_Sorted_CD8_Cells, oac701, oac702, oac70305, oac704, oac705, oac706, oac707, oac708, oac709,
oac710, oac711, oac712, oac713, oac714, OKyear, OPG, OPG_plasma, OPN, OPN_2013, OPN_plasma, OR_blood, Oral.glucose.inh, oralgluc, oralgluc2, oralgluc3, oralgluc4,
ORyear, othanthyp, othcoron, other, other2, OverallPlaquePhenotype, PAI1_pg_ug_2015, PAOD, PARC, patch, PCSK9_plasma, PDGF_BB_plasma, Percentage_CD14, Percentage_CD20,
Percentage_CD4, Percentage_CD8, Peripheral.interv, PKC, PLA2_plasma, plaquephenotype, positibl, PrimaryLast, PrimaryLast1, prostagl, PulsePressure, qual01, qual02,
qual0301, qual0302, qual0303, qual0304, qual0305, qual0306, qual0307, qual0308, qual0309, qual0310, qual0401, qual0402, qual0403, qual0404, qual0501, qual0502,
qual0503, qual06, qual07, qual08, qual0901, qual0902, qual0903, qual0904, qual0905, qual0906, qual0907, qual0908, qual0909, qual1010, qual1101, qual1102, qual1103,
qual1104, RAAS_med, RANTES, RANTES_pg_ug_2015, RANTES_plasma, Ras, RE50_01, RE70_01, Renine_recode, renineinh, restenos, restenosisOK, rheuma, rheuma2, rheuma3,
risk601, risk602, risk603, risk604, risk605, risk606, risk607, risk608, risk609, risk610, risk611, risk612, risk613, risk614, risk615, risk616, risk617, risk618,
risk619, risk620, SHBG, sICAM1, SMAD1_5_8, SMAD2, SMAD3, smc, smc_location, smc_macrophages_ratio, SMC.bin, smcmean0, SmokerCurrent, SmokerStatus, SmokingReported,
SmokingYearOR, stat3P, statin2, statines, ste3mext, sten1yr, sten3mo, stenose, stenosis_con_bin, Stenosis_contralateral, Stenosis_ipsilateral, Stroke_Dx,
Stroke_eitherside, Stroke_history, Stroke_Symptoms, StrokeTIA_Dx, StrokeTIA_history, StrokeTIA_Symptoms, STUDY_NUMBER, sympt, Sympt_latest, Sympt_worst, sympt1, sympt2,
sympt3, sympt4, Symptoms.3g, Symptoms.4g, Symptoms.5G, systolic, T_NUMBER, TARC, TAT_plasma, TC_2016, TC_all, TC_avg, TC_clinic, TC_dif, TC_final, TC_finalCU, TC_var,
Testosterone, TG_2016, TG_all, TG_avg, TG_clinic, TG_dif, TG_final, TG_finalCU, TG_var, TGF, TGFB, thrombos, thrombus, thrombus_location, thrombus_new,
thrombus_organization, thrombus_organization_v2, thrombus_percentage, thyros2, thyrosta, Time_event_OR, TimeOR_latest, TimeOR_latest_4g, TimeOR_worst, TimeOR_worst_4g,
TIMP1, TIMP2, TISNOW, TNFA, totalchol, totalcholesterol_source, tractdig, tractdig2, tractdig3, tractdig4, tractres, Treatment.DM, TREM1, triglyceride_source,
triglyceriden, Trop1, Trop1DT, Trop2, Trop2DT, Trop3, Trop3DT, TropmaxpostOK, TropoMax, TropoMaxDT, tropomaxpositief, TSratio_blood, TSratio_plaque, UPID,
validation_date, validation1, validation2, validation3, validation4, validation5, validation6, VAR00001, VEGFA_plasma, vegfa422, vessel_density,
vessel_density_additional, vessel_density_averaged, vessel_density_Timo2012, vessel_density_Timo2012_2, vessel_density_Timo2013, vitamin, vitamin2, vitb12, VRAGENLIJST,
vWF_plasma, WBC_THAW, Which.femoral.artery, Whichoperation, writtenIC, yearablo, yearablo2, yearablo3, yearace, yearace2, yearacet, yearanal, yearanal2, yearanal3,
yearangi, yearanta, yearanta2, yearanti, yearanti2, yearbblo, yearbblo2, yearcalc, yearcalc2, yearcalreg, yearcarb, yearchol, yearchol2, yearchol3, yearclau1,
yearclau2, yearclop, yearcom1, yearcom2, yearcom3, yearcort, yearcorthorm2, yearderm, yeardig, yeardig2, yeardig3, yeardig4, yeardipy, yeardiur, yeardiur2, yeardiur3,
yearerec, yeareye, yeargluc, yeargluc2, yeargluc3, yeargluc4, yeargrel, yearinsu, yeariron, yeariron2, yearneur, yearneur2, yearneur3, yearneur4, yearnitr, yearnitr2,
yearOR_bin_2010, YearOR_per2years, yearotant, yearotcor, yearoth2, yearothe, yearpros, yearpsy5, yearren, yearresp, yearrheu, yearrheu2, yearrheu3, yearsta2, yearstat,
yearthro, yearthyr, yearthyr2, yearvit2, yearvita, Yrs.no.smoking, Yrs.smokingAEDB[,"epmajor.30days"] <- AEDB$epmajor.3years
AEDB$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0
AEDB[,"epstroke.30days"] <- AEDB$epstroke.3years
AEDB$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0
AEDB[,"epcoronary.30days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0
AEDB[,"epcvdeath.30days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0
AEDB[,"epmajor.90days"] <- AEDB$epmajor.3years
AEDB$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0
AEDB[,"epstroke.90days"] <- AEDB$epstroke.3years
AEDB$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0
AEDB[,"epcoronary.90days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0
AEDB[,"epcvdeath.90days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0
detach(AEDB)
AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary",
"epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
"epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
"epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
rm(AEDB.temp)
attach(AEDB.CEA)The following objects are masked from AEDB.CEA (pos = 4):
ABI_70, ABI_max, ABI_mean, ABI_min, ABI_OP, ablock, ablock2, ablock3, aceinhib, aceinhib2, acetylsa, Adiponectin_pg_ug_2015, AE_AAA_bijzonderheden, Age, Age_Q, AgeSQR,
aid, AlcoholUse, Aldosteron_recode, alg10201, alg10202, alg10203, alg10204, alg10205, alg105, alg106, alg109, alg110, alg113, alg114, alg115, ALOX5, analg2, analg3,
analgeti, Ang2, angioii, ANGPT2, anti_apoA1_IgG, anti_apoA1_index, anti_apoA1_na, antiall, antiall2, antiarrh, antiarrh2, ANXA2, AP_Dx, AP_Dx1, AP_Dx2, APOB, artercon,
Artery_summary, arteryop, AsymptSympt, AsymptSympt2G, bblock, bblock2, blocko, blocksnr, BMI, BMI_US, BMI_WHO, BMI30ormore, brain401, brain402, brain403, brain404,
brain405, brain406, brain407, brain408, brain409, brain410, brain411, brain412, brain413, brn40701, bspoed, CAD_Dx, CAD_Dx1, CAD_Dx2, CAD_history, CADPAOD_history,
Calc.bin, calcification, CalcificationPlaque, calcium, calcium2, calreg, carbasal, cardioembolic, Caspase3_7, CAV1, CD44, CD44V3, CEA_or_CAS, CEL, CFD_recalc, cholverl,
cholverl2, cholverl3, CI_history, clau1, clau2, Claudication, clopidog, CML, collagen, Collagen.bin, CollagenPlaque, combi1, combi2, combi3, comorbidity.DM, concablo,
concablo2, concablo3, concace2, concacei, concacet, concalle, concanal, concanal2, concanal3, concangi, concanta2, concanti, concanti2, concbblo, concbblo2, conccalc,
conccalc2, conccalreg, conccarb, concchol, concchol2, concchol3, concclau1, concclau2, concclop, conccom1, conccom2, conccom3, conccort, conccorthorm2, concderm,
concdig, concdig2, concdig3, concdig4, concdipy, concdiur, concdiur2, concdiur3, concerec, conceye, concgluc, concgluc2, concgluc3, concgluc4, concgrel, concinsu,
conciron, conciron2, concneur, concneur2, concneur3, concneur4, concnitr, concnitr2, concotant, concotcor, concoth2, concothe, concpros, concpsy5, concren, concresp,
concrheu, concrheu2, concrheu3, concsta2, concstat, concthro, concthyr, concthyr2, concvit2, concvita, Contralateral_surgery, conwhen, corticos, cortihorm2, creat,
crp_all, CRP_avg, CRP_dif, crp_source, CRP_var, CST3_pg_ug, CST3_serum_luminex, CTGF, cTNI_plasma, CTSA, CTSB, CTSL1, CTSS, cyr61, date_ic_patient, date_ic_researcher,
Date.of.birth, date.previous.operation, date1yr, date3mon, dateapprox_latest, dateapprox_worst, dateapprox1, dateapprox2, dateapprox3, dateapprox4, dateend1, dateend2,
dateend3, dateend4, dateend5, dateend6, dateexact_latest, dateexact_worst, dateexact1, dateexact2, dateexact3, dateexact4, dateok, dermacor, DiabetesStatus, diastoli,
diet801, diet802, diet803, diet804, diet805, diet806, diet807, diet808, diet809, diet810, diet811, diet812, diet813, diet814, diet815, diet816, diet817, diet818,
diet819, diet820, diet821, diet822, diet823, diet824, dipyridi, diuretic, diuretic2, diuretic3, DM, DM.composite, duaalantiplatelet, duplend, eaindexl, eaindexr,
eCigarettes, edaplaqu_recalc, edavrspl, EGR, EMMPRIN_45kD, EMMPRIN_58kD, ENDOGLIN, endpoint1, endpoint2, endpoint3, endpoint4, endpoint5, endpoint6, Eotaxin1, EP_CAD,
ep_cad_t_30days, ep_cad_t_3years, EP_CAD_time, ep_cad.30days, EP_CI, ep_ci_t_30days, ep_ci_t_3years, EP_CI_time, ep_com_t_30days, ep_com_t_3years, EP_composite,
EP_composite_time, EP_coronary, ep_coronary_t_30days, ep_coronary_t_3years, ep_coronary_t_90days, EP_coronary_time, EP_CVdeath, ep_cvdeath_t_30days,
ep_cvdeath_t_3years, ep_cvdeath_t_90days, EP_CVdeath_time, EP_death, ep_death_t_30days, ep_death_t_3years, EP_death_time, EP_fatalCVA, ep_fatalCVA_t_30days,
ep_fatalCVA_t_3years, EP_fatalCVA_time, EP_hemorrhagic_stroke, ep_hemorrhagic_stroke_t_3years, EP_hemorrhagic_stroke_time, ep_hemorrhagic_stroke.3years,
EP_ischemic_stroke, ep_ischemic_stroke_t_3years, EP_ischemic_stroke_time, ep_ischemic_stroke.3years, EP_leg_amputation, EP_leg_amputation_time,
ep_legamputation_t_30days, ep_legamputation_t_3years, EP_major, ep_major_t_30days, ep_major_t_3years, ep_major_t_90days, EP_major_time, EP_MI, ep_mi_t_30days,
ep_mi_t_3years, EP_MI_time, EP_nonstroke_event, EP_nonstroke_event_time, ep_nonstroke_t_3years, EP_peripheral, ep_peripheral_t_30days, ep_peripheral_t_3years,
EP_peripheral_time, EP_pta, ep_pta_t_30days, ep_pta_t_3years, EP_pta_time, EP_stroke, ep_stroke_t_30days, ep_stroke_t_3years, ep_stroke_t_90days, EP_stroke_time,
EP_strokeCVdeath, ep_strokeCVdeath_t_30days, ep_strokeCVdeath_t_3years, EP_strokeCVdeath_time, EP_strokedeath, ep_strokedeath_t_30days, ep_strokedeath_t_3years,
EP_strokedeath_time, ePackYearsSmoking, epcad.3years, epci.30days, epci.3years, epcom.30days, epcom.3years, epcoronary.30days, epcoronary.3years, epcoronary.90days,
epcvdeath.30days, epcvdeath.3years, epcvdeath.90days, epdeath.30days, epdeath.3years, epfatalCVA.30days, epfatalCVA.3years, eplegamputation.30days,
eplegamputation.3years, epmajor.30days, epmajor.3years, epmajor.90days, epmi.30days, epmi.3years, epnonstroke.3years, epperipheral.30days, epperipheral.3years,
eppta.30days, eppta.3years, epstroke.30days, epstroke.3years, epstroke.90days, epstrokeCVdeath.30days, epstrokeCVdeath.3years, epstrokedeath.30days,
epstrokedeath.3years, erec, Estradiol, everstroke_composite, Everstroke_Ipsilateral, exer901, exer902, exer903, exer904, exer905, exer906, exer9071, exer9072, exer9073,
exer9074, exer9075, exer9076, exer908, exer909, exer910, eyedrop, EZis, FABP_serum, FABP4, FABP4_pg_ug, FABP4_serum_luminex, fat, Fat.bin_10, Fat.bin_40, Fat10Perc,
Femoral.interv, FH_AAA_broth, FH_AAA_comp, FH_AAA_mat, FH_AAA_parent, FH_AAA_pat, FH_AAA_sibling, FH_AAA_sis, FH_amp_broth, FH_amp_comp, FH_amp_mat, FH_amp_parent,
FH_amp_pat, FH_amp_sibling, FH_amp_sis, FH_CAD_broth, FH_CAD_comp, FH_CAD_mat, FH_CAD_parent, FH_CAD_pat, FH_CAD_sibling, FH_CAD_sis, FH_corcalc_broth, FH_corcalc_comp,
FH_corcalc_mat, FH_corcalc_parent, FH_corcalc_pat, FH_corcalc_sibling, FH_corcalc_sis, FH_CVD_broth, FH_CVD_comp, FH_CVD_mat, FH_CVD_parent, FH_CVD_pat, FH_CVD_sibling,
FH_CVD_sis, FH_CVdeath_broth, FH_CVdeath_comp, FH_CVdeath_mat, FH_CVdeath_parent, FH_CVdeath_pat, FH_CVdeath_sibling, FH_CVdeath_sis, FH_DM_broth, FH_DM_comp,
FH_DM_mat, FH_DM_parent, FH_DM_pat, FH_DM_sibling, FH_DM_sis, FH_HC_broth, FH_HC_comp, FH_HC_mat, FH_HC_parent, FH_HC_pat, FH_HC_sibling, FH_HC_sis, FH_HT_broth,
FH_HT_comp, FH_HT_mat, FH_HT_parent, FH_HT_pat, FH_HT_sibling, FH_HT_sis, FH_MI_broth, FH_MI_comp, FH_MI_mat, FH_MI_parent, FH_MI_pat, FH_MI_sibling, FH_MI_sis,
FH_otherCVD_broth, FH_otherCVD_comp, FH_otherCVD_mat, FH_otherCVD_parent, FH_otherCVD_pat, FH_otherCVD_sibling, FH_otherCVD_sis, FH_PAD_broth, FH_PAD_comp, FH_PAD_mat,
FH_PAD_parent, FH_PAD_pat, FH_PAD_sibling, FH_PAD_sis, FH_PAV_broth, FH_PAV_comp, FH_PAV_mat, FH_PAV_parent, FH_PAV_pat, FH_PAV_sibling, FH_PAV_sis, FH_POB_broth,
FH_POB_comp, FH_POB_mat, FH_POB_parent, FH_POB_pat, FH_POB_sibling, FH_POB_sis, FH_risk_broth, FH_risk_comp, FH_risk_mat, FH_risk_parent, FH_risk_pat, FH_risk_sibling,
FH_risk_sis, FH_Stroke_broth, FH_Stroke_comp, FH_Stroke_mat, FH_Stroke_parent, FH_Stroke_pat, FH_Stroke_sibling, FH_Stroke_sis, FH_tromb_broth, FH_tromb_comp,
FH_tromb_mat, FH_tromb_parent, FH_tromb_pat, FH_tromb_sibling, FH_tromb_sis, filter_$, folicaci, followup1, followup2, followup3, Fontaine, FU_check, FU_check_date,
FU.cutt.off.30days, FU.cutt.off.3years, FU.cutt.off.90days, FU1JAAR, FU2JAAR, FU3JAAR, FURIN_low, FURIN_up, GDF15_plasma, geen_med, Gender, GFR_CG, GFR_MDRD, glucose,
GR_Segment, GrB_plaque, GrB_serum, grel, GrK_plaque, GrK_serum, GrM_plaque, GrM_serum, HA, hb, HDAC9, HDL, HDL_2016, HDL_all, HDL_avg, HDL_clinic, HDL_dif, HDL_final,
HDL_finalCU, hdl_source, HDL_var, heart300, heart301, heart302, heart303, heart304, heart305, heart306, heart307, heart308, heart309, heart310, heart311, heart312,
heart313, heart314, heart315, heart316, heart317, heart318, heart319, heart320, heart321, heart322, heart323, heart324, heart325, heart326, heart327, heart328, HIF1A,
ho1, homocys, Hospital, hrt31301, hsCRP_plasma, ht, HYAL55KD, HYALURON, Hypertension.composite, Hypertension.drugs, Hypertension.selfreport,
Hypertension.selfreportdrug, Hypertension1, Hypertension2, IL1_Beta, IL10, IL12, IL13, IL17, IL2, IL21, IL4, IL5, IL6, IL6_pg_ug_2015, IL6R_pg_ug_2015, IL8,
IL8_pg_ug_2015, IL9, indexsymptoms_latest, indexsymptoms_latest_4g, indexsymptoms_worst, indexsymptoms_worst_4g, INFG, informedconsent, insulin, insuline, INVULDAT,
IP10, IPH, IPH_extended.bin, IPH.bin, ironfoli, ironfoli2, KDOQI, latest, LDL, LDL_2016, LDL_all, LDL_avg, LDL_clinic, LDL_dif, LDL_final, LDL_finalCU, ldl_source,
LDL_var, leg501, leg502, leg503, leg504, leg505, leg506, leg507, leg508, leg509, leg510, leg511, leg512, leg513, leg514, leg515, leg516, leg517, leg518, leg519, leg520,
LMW1STME, LTB4, LTB4R, macmean0, macrophages, Macrophages_LN, macrophages_location, Macrophages_rank, Macrophages.bin, MAP, Mast_cells_plaque, max.followup, MCP1,
MCP1_pg_ug_2015, MCP1_pg_ug_2015_LN, MCP1_pg_ug_2015_rank, MCP1_rank, MCSF_pg_ug_2015, MDC, Med_notes, Med.ablock, Med.ACE_inh, Med.acetylsal, Med.acetylsal_Combi1,
Med.acetylsal_Combi2, Med.acetylsal_Combi3, Med.ADPinh, Med.all.antiplatelet, Med.angiot2.antag, Med.antiarrh, Med.anticoagulants, Med.ascal, Med.aspirin.derived,
Med.bblocker, Med.calc_antag, Med.dipyridamole, Med.diuretic, Med.LLD, Med.nitrate, Med.otheranthyp, Med.renin, Med.statin, Med.statin.derived, Med.Statin.LLD,
Med.statin2, MedHx_CVD, media, MG_H1, MI_Dx, MI_Dx1, MI_Dx2, MIF, MIG, MIP1a, miRNA100_RNU19, miRNA100_RNU48, miRNA155_RNU19, miRNA155_RNU48, MMP14, MMP2, MMP2TIMP2,
MMP8, MMP9, MMP9TIMP1, MPO_plasma, MRP_14, MRP_8, MRP_8_14C, MRP_8_14C_buhlmann, MRP14_plasma, MRP8_14C_plasma, MRP8_plasma, negatibl, neuropsy, neuropsy2, neuropsy3,
neuropsy4, neurpsy5, neutrophils, NGAL, NGAL_low, NGAL_MMP9_complex, NGAL_MMP9_local, NGAL_MMP9_peripheral, NGAL_total, NGAL_up, nitrate, nitrate2, NOD1, NOD2,
nogobt1_recalculated, NTproBNP_plasma, Number_Events_Sorter, Number_Sorted_CD14, Number_Sorted_CD20, Number_Sorted_CD4_Cells, Number_Sorted_CD8_Cells, oac701, oac702,
oac70305, oac704, oac705, oac706, oac707, oac708, oac709, oac710, oac711, oac712, oac713, oac714, OKyear, OPG, OPG_plasma, OPN, OPN_2013, OPN_plasma, OR_blood,
Oral.glucose.inh, oralgluc, oralgluc2, oralgluc3, oralgluc4, ORyear, othanthyp, othcoron, other, other2, OverallPlaquePhenotype, PAI1_pg_ug_2015, PAOD, PARC, patch,
PCSK9_plasma, PDGF_BB_plasma, Percentage_CD14, Percentage_CD20, Percentage_CD4, Percentage_CD8, Peripheral.interv, PKC, PLA2_plasma, plaquephenotype, positibl,
PrimaryLast, PrimaryLast1, prostagl, PulsePressure, qual01, qual02, qual0301, qual0302, qual0303, qual0304, qual0305, qual0306, qual0307, qual0308, qual0309, qual0310,
qual0401, qual0402, qual0403, qual0404, qual0501, qual0502, qual0503, qual06, qual07, qual08, qual0901, qual0902, qual0903, qual0904, qual0905, qual0906, qual0907,
qual0908, qual0909, qual1010, qual1101, qual1102, qual1103, qual1104, RAAS_med, RANTES, RANTES_pg_ug_2015, RANTES_plasma, Ras, RE50_01, RE70_01, Renine_recode,
renineinh, restenos, restenosisOK, rheuma, rheuma2, rheuma3, risk601, risk602, risk603, risk604, risk605, risk606, risk607, risk608, risk609, risk610, risk611, risk612,
risk613, risk614, risk615, risk616, risk617, risk618, risk619, risk620, SHBG, sICAM1, SMAD1_5_8, SMAD2, SMAD3, smc, SMC_LN, smc_location, smc_macrophages_ratio,
SMC_rank, SMC.bin, smcmean0, SmokerCurrent, SmokerStatus, SmokingReported, SmokingYearOR, stat3P, statin2, statines, ste3mext, sten1yr, sten3mo, stenose,
stenosis_con_bin, Stenosis_contralateral, Stenosis_ipsilateral, Stroke_Dx, Stroke_eitherside, Stroke_history, Stroke_Symptoms, StrokeTIA_Dx, StrokeTIA_history,
StrokeTIA_Symptoms, STUDY_NUMBER, sympt, Sympt_latest, Sympt_worst, sympt1, sympt2, sympt3, sympt4, Symptoms.3g, Symptoms.4g, Symptoms.5G, systolic, T_NUMBER, TARC,
TAT_plasma, TC_2016, TC_all, TC_avg, TC_clinic, TC_dif, TC_final, TC_finalCU, TC_var, Testosterone, TG_2016, TG_all, TG_avg, TG_clinic, TG_dif, TG_final, TG_finalCU,
TG_var, TGF, TGFB, thrombos, thrombus, thrombus_location, thrombus_new, thrombus_organization, thrombus_organization_v2, thrombus_percentage, thyros2, thyrosta,
Time_event_OR, TimeOR_latest, TimeOR_latest_4g, TimeOR_worst, TimeOR_worst_4g, TIMP1, TIMP2, TISNOW, TNFA, totalchol, totalcholesterol_source, tractdig, tractdig2,
tractdig3, tractdig4, tractres, Treatment.DM, TREM1, triglyceride_source, triglyceriden, Trop1, Trop1DT, Trop2, Trop2DT, Trop3, Trop3DT, TropmaxpostOK, TropoMax,
TropoMaxDT, tropomaxpositief, TSratio_blood, TSratio_plaque, UPID, validation_date, validation1, validation2, validation3, validation4, validation5, validation6,
VAR00001, VEGFA_plasma, vegfa422, vessel_density, vessel_density_additional, vessel_density_averaged, vessel_density_Timo2012, vessel_density_Timo2012_2,
vessel_density_Timo2013, VesselDensity_LN, VesselDensity_rank, vitamin, vitamin2, vitb12, VRAGENLIJST, vWF_plasma, WBC_THAW, Which.femoral.artery, Whichoperation,
writtenIC, yearablo, yearablo2, yearablo3, yearace, yearace2, yearacet, yearanal, yearanal2, yearanal3, yearangi, yearanta, yearanta2, yearanti, yearanti2, yearbblo,
yearbblo2, yearcalc, yearcalc2, yearcalreg, yearcarb, yearchol, yearchol2, yearchol3, yearclau1, yearclau2, yearclop, yearcom1, yearcom2, yearcom3, yearcort,
yearcorthorm2, yearderm, yeardig, yeardig2, yeardig3, yeardig4, yeardipy, yeardiur, yeardiur2, yeardiur3, yearerec, yeareye, yeargluc, yeargluc2, yeargluc3, yeargluc4,
yeargrel, yearinsu, yeariron, yeariron2, yearneur, yearneur2, yearneur3, yearneur4, yearnitr, yearnitr2, yearOR_bin_2010, YearOR_per2years, yearotant, yearotcor,
yearoth2, yearothe, yearpros, yearpsy5, yearren, yearresp, yearrheu, yearrheu2, yearrheu3, yearsta2, yearstat, yearthro, yearthyr, yearthyr2, yearvit2, yearvita,
Yrs.no.smoking, Yrs.smokingAEDB.CEA[,"epmajor.30days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0
AEDB.CEA[,"epstroke.30days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0
AEDB.CEA[,"epcoronary.30days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0
AEDB.CEA[,"epcvdeath.30days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0
AEDB.CEA[,"epmajor.90days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0
AEDB.CEA[,"epstroke.90days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0
AEDB.CEA[,"epcoronary.90days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0
AEDB.CEA[,"epcvdeath.90days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0
detach(AEDB.CEA)
AEDB.CEA.temp <- subset(AEDB.CEA, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary",
"epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
"epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
"epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)
DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)
rm(AEDB.CEA.temp)
Sanity checks
First we do some sanity checks and inventory the time-to-event and event variables.
# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")
install.packages.auto("survminer")
install.packages.auto("Hmisc")
cat("* Creating function to summarize Cox regression and prepare container for results.")* Creating function to summarize Cox regression and prepare container for results.# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, protein){
cat("Summarizing Cox regression results for '", protein ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
if (nrow(summary(coxfit)$coefficients) == 1) {
output = c(protein, rep(NA,8))
cat("Model not fitted; probably singular.\n")
}else {
cat("Collecting data.\n\n")
cox.sum <- summary(coxfit)
cox.effectsize = cox.sum$coefficients[1,1]
cox.SE = cox.sum$coefficients[1,3]
cox.HReffect = cox.sum$coefficients[1,2]
cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
cox.zvalue = cox.sum$coefficients[1,4]
cox.pvalue = cox.sum$coefficients[1,5]
cox.sample_size = cox.sum$n
cox.nevents = cox.sum$nevent
output = c(DATASET, OUTCOME, protein, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
cat("We have collected the following:\n")
cat("Dataset used..............:", DATASET, "\n")
cat("Outcome analyzed..........:", OUTCOME, "\n")
cat("Protein...................:", protein, "\n")
cat("Effect size...............:", round(cox.effectsize, 6), "\n")
cat("Standard error............:", round(cox.SE, 6), "\n")
cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
cat("T-value...................:", round(cox.zvalue, 6), "\n")
cat("P-value...................:", signif(cox.pvalue, 8), "\n")
cat("Sample size in model......:", cox.sample_size, "\n")
cat("Number of events..........:", cox.nevents, "\n")
}
return(output)
print(output)
}
times = c("ep_major_t_3years",
"ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")
endpoints = c("epmajor.3years",
"epstroke.3years", "epcoronary.3years", "epcvdeath.3years")
cat("* Check the cases per event type - for sanity.")* Check the cases per event type - for sanity.for (events in endpoints){
require(labelled)
print(paste0("Printing the summary of: ",events))
# print(summary(AEDB.CEA[,events]))
print(table(AEDB.CEA[,events]))
}[1] "Printing the summary of: epmajor.3years"
0 1
2035 265
[1] "Printing the summary of: epstroke.3years"
0 1
2171 130
[1] "Printing the summary of: epcoronary.3years"
0 1
2119 182
[1] "Printing the summary of: epcvdeath.3years"
0 1
2210 90 cat("* Check distribution of events over time - for sanity.")* Check distribution of events over time - for sanity.for (eventtimes in times){
print(paste0("Printing the summary of: ",eventtimes))
print(summary(AEDB.CEA[,eventtimes]))
}[1] "Printing the summary of: ep_major_t_3years"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 2.710 3.000 2.573 3.000 3.000 125
[1] "Printing the summary of: ep_stroke_t_3years"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 2.877 3.000 2.624 3.000 3.000 125
[1] "Printing the summary of: ep_coronary_t_3years"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 2.784 3.000 2.622 3.000 3.000 125
[1] "Printing the summary of: ep_cvdeath_t_3years"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00274 2.91233 3.00000 2.70902 3.00000 3.00000 125 for (eventtime in times){
print(paste0("Printing the distribution of: ",eventtime))
p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
main = eventtime, bins = 15,
xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal())
print(p)
ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}[1] "Printing the distribution of: ep_major_t_3years"
[1] "Printing the distribution of: ep_stroke_t_3years"
[1] "Printing the distribution of: ep_coronary_t_3years"
[1] "Printing the distribution of: ep_cvdeath_t_3years"
times30 = c("ep_major_t_30days",
"ep_stroke_t_30days", "ep_coronary_t_30days", "ep_cvdeath_t_30days")
endpoints30 = c("epmajor.30days",
"epstroke.30days", "epcoronary.30days", "epcvdeath.30days")
cat("* Check the cases per event type - for sanity.")* Check the cases per event type - for sanity.for (events in endpoints30){
print(paste0("Printing the summary of: ",events))
# print(summary(AEDB.CEA[,events]))
print(table(AEDB.CEA[,events]))
}[1] "Printing the summary of: epmajor.30days"
0 1
2222 78
[1] "Printing the summary of: epstroke.30days"
0 1
2248 53
[1] "Printing the summary of: epcoronary.30days"
0 1
2267 34
[1] "Printing the summary of: epcvdeath.30days"
0 1
2288 12 cat("* Check distribution of events over time - for sanity.")* Check distribution of events over time - for sanity.for (eventtimes in times30){
print(paste0("Printing the summary of: ",eventtimes))
print(summary(AEDB.CEA[,eventtimes]))
}[1] "Printing the summary of: ep_major_t_30days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 30.00 30.00 29.09 30.00 30.00 125
[1] "Printing the summary of: ep_stroke_t_30days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 30.00 30.00 29.32 30.00 30.00 125
[1] "Printing the summary of: ep_coronary_t_30days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 30.00 30.00 29.54 30.00 30.00 125
[1] "Printing the summary of: ep_cvdeath_t_30days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1.001 30.000 30.000 29.854 30.000 30.000 125 for (eventtime in times30){
print(paste0("Printing the distribution of: ",eventtime))
p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
main = eventtime, bins = 15,
xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal())
print(p)
ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer30Days.",eventtime,".pdf"), plot = last_plot())
}[1] "Printing the distribution of: ep_major_t_30days"
[1] "Printing the distribution of: ep_stroke_t_30days"
[1] "Printing the distribution of: ep_coronary_t_30days"
[1] "Printing the distribution of: ep_cvdeath_t_30days"
times90 = c("ep_major_t_90days",
"ep_stroke_t_90days", "ep_coronary_t_90days", "ep_cvdeath_t_90days")
endpoints90 = c("epmajor.90days",
"epstroke.90days", "epcoronary.90days", "epcvdeath.90days")
cat("* Check the cases per event type - for sanity.")* Check the cases per event type - for sanity.for (events in endpoints90){
print(paste0("Printing the summary of: ",events))
# print(summary(AEDB.CEA[,events]))
print(table(AEDB.CEA[,events]))
}[1] "Printing the summary of: epmajor.90days"
0 1
2206 94
[1] "Printing the summary of: epstroke.90days"
0 1
2241 60
[1] "Printing the summary of: epcoronary.90days"
0 1
2257 44
[1] "Printing the summary of: epcvdeath.90days"
0 1
2281 19 cat("* Check distribution of events over time - for sanity.")* Check distribution of events over time - for sanity.for (eventtimes in times90){
print(paste0("Printing the summary of: ",eventtimes))
print(summary(AEDB.CEA[,eventtimes]))
}[1] "Printing the summary of: ep_major_t_90days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 90.00 90.00 86.75 90.00 90.00 125
[1] "Printing the summary of: ep_stroke_t_90days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 90.00 90.00 87.51 90.00 90.00 125
[1] "Printing the summary of: ep_coronary_t_90days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 90.00 90.00 88.21 90.00 90.00 125
[1] "Printing the summary of: ep_cvdeath_t_90days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1.001 90.000 90.000 89.320 90.000 90.000 125 for (eventtime in times90){
print(paste0("Printing the distribution of: ",eventtime))
p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
main = eventtime, bins = 15,
xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal())
print(p)
ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer90Days.",eventtime,".pdf"), plot = last_plot())
}[1] "Printing the distribution of: ep_major_t_90days"
[1] "Printing the distribution of: ep_stroke_t_90days"
[1] "Printing the distribution of: ep_coronary_t_90days"
[1] "Printing the distribution of: ep_cvdeath_t_90days"
Cox regressions
Let’s perform the actual Cox-regressions. We will apply a couple of models:
- Model 1: adjusted for age, sex, and year of surgery
- Model 2: adjusted for age, sex, year of surgery, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
3 years follow-up
MODEL 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
eptime = times[i]
ep = endpoints[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 1 (Simple model)
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
# ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [years]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}* Analyzing the effect of plaque proteins on [epmajor.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 3 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34102,0.00105) [ 0.00105,3.34102]
599 599
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 1186, number of events= 139
(1237 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 1.091e-01 1.115e+00 1.852e-01 0.589 0.555839
Age 3.546e-02 1.036e+00 1.018e-02 3.484 0.000493 ***
Gendermale 3.439e-01 1.410e+00 2.002e-01 1.718 0.085867 .
ORdate_year2003 -3.663e-01 6.933e-01 4.208e-01 -0.870 0.384044
ORdate_year2004 -6.643e-01 5.146e-01 4.191e-01 -1.585 0.112971
ORdate_year2005 -2.289e-01 7.954e-01 3.944e-01 -0.580 0.561666
ORdate_year2006 -2.894e-01 7.487e-01 4.011e-01 -0.721 0.470705
ORdate_year2007 -1.156e+00 3.148e-01 4.752e-01 -2.432 0.015011 *
ORdate_year2008 -2.303e-01 7.943e-01 4.157e-01 -0.554 0.579524
ORdate_year2009 -1.074e+00 3.415e-01 4.938e-01 -2.176 0.029586 *
ORdate_year2010 -8.627e-01 4.220e-01 4.793e-01 -1.800 0.071836 .
ORdate_year2011 -9.679e-01 3.799e-01 4.630e-01 -2.091 0.036570 *
ORdate_year2012 -1.132e-01 8.930e-01 4.019e-01 -0.282 0.778167
ORdate_year2013 -4.015e-01 6.693e-01 6.625e-01 -0.606 0.544476
ORdate_year2014 -1.360e+01 1.235e-06 1.979e+03 -0.007 0.994515
ORdate_year2015 NA NA 0.000e+00 NA NA
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 1.115e+00 8.966e-01 0.7757 1.6035
Age 1.036e+00 9.652e-01 1.0156 1.0570
Gendermale 1.410e+00 7.090e-01 0.9526 2.0883
ORdate_year2003 6.933e-01 1.442e+00 0.3039 1.5817
ORdate_year2004 5.146e-01 1.943e+00 0.2263 1.1702
ORdate_year2005 7.954e-01 1.257e+00 0.3672 1.7231
ORdate_year2006 7.487e-01 1.336e+00 0.3411 1.6436
ORdate_year2007 3.148e-01 3.176e+00 0.1240 0.7990
ORdate_year2008 7.943e-01 1.259e+00 0.3516 1.7940
ORdate_year2009 3.415e-01 2.928e+00 0.1297 0.8990
ORdate_year2010 4.220e-01 2.370e+00 0.1650 1.0796
ORdate_year2011 3.799e-01 2.632e+00 0.1533 0.9413
ORdate_year2012 8.930e-01 1.120e+00 0.4062 1.9630
ORdate_year2013 6.693e-01 1.494e+00 0.1827 2.4521
ORdate_year2014 1.235e-06 8.098e+05 0.0000 Inf
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Concordance= 0.633 (se = 0.023 )
Likelihood ratio test= 33.73 on 15 df, p=0.004
Wald test = 31.56 on 15 df, p=0.007
Score (logrank) test = 32.54 on 15 df, p=0.005
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.10911
Standard error............: 0.185237
Odds ratio (effect size)..: 1.115
Lower 95% CI..............: 0.776
Upper 95% CI..............: 1.603
T-value...................: 0.589033
P-value...................: 0.555839
Sample size in model......: 1186
Number of events..........: 139
> processing [MCP1_pg_ml_2015_rank]; 2 out of 3 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 1187, number of events= 140
(1236 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] -1.243e-02 9.876e-01 1.897e-01 -0.066 0.947733
Age 3.527e-02 1.036e+00 1.016e-02 3.472 0.000516 ***
Gendermale 3.589e-01 1.432e+00 2.009e-01 1.787 0.073979 .
ORdate_year2003 -3.728e-01 6.888e-01 4.212e-01 -0.885 0.376068
ORdate_year2004 -7.057e-01 4.938e-01 4.194e-01 -1.683 0.092454 .
ORdate_year2005 -2.619e-01 7.696e-01 3.947e-01 -0.664 0.506906
ORdate_year2006 -3.132e-01 7.311e-01 3.994e-01 -0.784 0.432964
ORdate_year2007 -1.154e+00 3.155e-01 4.752e-01 -2.428 0.015187 *
ORdate_year2008 -2.196e-01 8.028e-01 4.162e-01 -0.528 0.597643
ORdate_year2009 -1.063e+00 3.453e-01 4.951e-01 -2.148 0.031752 *
ORdate_year2010 -8.290e-01 4.365e-01 4.784e-01 -1.733 0.083073 .
ORdate_year2011 -9.444e-01 3.889e-01 4.641e-01 -2.035 0.041870 *
ORdate_year2012 -4.363e-02 9.573e-01 4.006e-01 -0.109 0.913277
ORdate_year2013 -3.635e-01 6.953e-01 6.626e-01 -0.549 0.583345
ORdate_year2014 -1.355e+01 1.305e-06 1.976e+03 -0.007 0.994528
ORdate_year2015 NA NA 0.000e+00 NA NA
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 9.876e-01 1.013e+00 0.6810 1.4323
Age 1.036e+00 9.653e-01 1.0155 1.0567
Gendermale 1.432e+00 6.984e-01 0.9658 2.1227
ORdate_year2003 6.888e-01 1.452e+00 0.3017 1.5725
ORdate_year2004 4.938e-01 2.025e+00 0.2170 1.1234
ORdate_year2005 7.696e-01 1.299e+00 0.3551 1.6679
ORdate_year2006 7.311e-01 1.368e+00 0.3342 1.5993
ORdate_year2007 3.155e-01 3.170e+00 0.1243 0.8006
ORdate_year2008 8.028e-01 1.246e+00 0.3551 1.8149
ORdate_year2009 3.453e-01 2.896e+00 0.1309 0.9113
ORdate_year2010 4.365e-01 2.291e+00 0.1709 1.1146
ORdate_year2011 3.889e-01 2.571e+00 0.1566 0.9659
ORdate_year2012 9.573e-01 1.045e+00 0.4366 2.0991
ORdate_year2013 6.953e-01 1.438e+00 0.1897 2.5480
ORdate_year2014 1.305e-06 7.663e+05 0.0000 Inf
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Concordance= 0.633 (se = 0.023 )
Likelihood ratio test= 34.49 on 15 df, p=0.003
Wald test = 32.37 on 15 df, p=0.006
Score (logrank) test = 33.42 on 15 df, p=0.004
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: -0.012433
Standard error............: 0.189652
Odds ratio (effect size)..: 0.988
Lower 95% CI..............: 0.681
Upper 95% CI..............: 1.432
T-value...................: -0.065554
P-value...................: 0.9477325
Sample size in model......: 1187
Number of events..........: 140
> processing [MCP1_rank]; 3 out of 3 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 549, number of events= 70
(1874 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -0.27322 0.76093 0.25377 -1.077 0.2816
Age 0.02471 1.02502 0.01495 1.653 0.0983 .
Gendermale 0.88580 2.42492 0.34390 2.576 0.0100 *
ORdate_year2003 -0.67512 0.50910 0.40377 -1.672 0.0945 .
ORdate_year2004 -0.65763 0.51808 0.38433 -1.711 0.0871 .
ORdate_year2005 -0.51750 0.59601 0.37326 -1.386 0.1656
ORdate_year2006 -0.08358 0.91982 0.52051 -0.161 0.8724
ORdate_year2007 NA NA 0.00000 NA NA
ORdate_year2008 NA NA 0.00000 NA NA
ORdate_year2009 NA NA 0.00000 NA NA
ORdate_year2010 NA NA 0.00000 NA NA
ORdate_year2011 NA NA 0.00000 NA NA
ORdate_year2012 NA NA 0.00000 NA NA
ORdate_year2013 NA NA 0.00000 NA NA
ORdate_year2014 NA NA 0.00000 NA NA
ORdate_year2015 NA NA 0.00000 NA NA
ORdate_year2016 NA NA 0.00000 NA NA
ORdate_year2017 NA NA 0.00000 NA NA
ORdate_year2018 NA NA 0.00000 NA NA
ORdate_year2019 NA NA 0.00000 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 0.7609 1.3142 0.4627 1.251
Age 1.0250 0.9756 0.9954 1.055
Gendermale 2.4249 0.4124 1.2359 4.758
ORdate_year2003 0.5091 1.9643 0.2307 1.123
ORdate_year2004 0.5181 1.9302 0.2439 1.100
ORdate_year2005 0.5960 1.6778 0.2868 1.239
ORdate_year2006 0.9198 1.0872 0.3316 2.551
ORdate_year2007 NA NA NA NA
ORdate_year2008 NA NA NA NA
ORdate_year2009 NA NA NA NA
ORdate_year2010 NA NA NA NA
ORdate_year2011 NA NA NA NA
ORdate_year2012 NA NA NA NA
ORdate_year2013 NA NA NA NA
ORdate_year2014 NA NA NA NA
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Concordance= 0.626 (se = 0.033 )
Likelihood ratio test= 16.42 on 7 df, p=0.02
Wald test = 15.26 on 7 df, p=0.03
Score (logrank) test = 15.86 on 7 df, p=0.03
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_rank
Effect size...............: -0.273217
Standard error............: 0.253769
Odds ratio (effect size)..: 0.761
Lower 95% CI..............: 0.463
Upper 95% CI..............: 1.251
T-value...................: -1.076636
P-value...................: 0.281643
Sample size in model......: 549
Number of events..........: 70
* Analyzing the effect of plaque proteins on [epstroke.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 3 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34102,0.00105) [ 0.00105,3.34102]
599 599
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 1186, number of events= 73
(1237 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 3.059e-01 1.358e+00 2.579e-01 1.186 0.23552
Age 3.694e-02 1.038e+00 1.389e-02 2.659 0.00783 **
Gendermale 6.936e-02 1.072e+00 2.599e-01 0.267 0.78957
ORdate_year2003 -2.527e-01 7.767e-01 5.861e-01 -0.431 0.66634
ORdate_year2004 -4.252e-01 6.536e-01 5.780e-01 -0.736 0.46188
ORdate_year2005 6.441e-02 1.067e+00 5.382e-01 0.120 0.90474
ORdate_year2006 -6.122e-02 9.406e-01 5.516e-01 -0.111 0.91164
ORdate_year2007 -8.964e-01 4.080e-01 6.335e-01 -1.415 0.15706
ORdate_year2008 -4.147e-01 6.605e-01 6.076e-01 -0.683 0.49490
ORdate_year2009 -1.757e+01 2.333e-08 2.052e+03 -0.009 0.99317
ORdate_year2010 -4.941e-01 6.101e-01 6.123e-01 -0.807 0.41976
ORdate_year2011 -8.580e-01 4.240e-01 6.362e-01 -1.349 0.17749
ORdate_year2012 -1.869e-01 8.295e-01 5.739e-01 -0.326 0.74469
ORdate_year2013 -8.291e-01 4.364e-01 1.099e+00 -0.754 0.45068
ORdate_year2014 -1.757e+01 2.349e-08 2.099e+04 -0.001 0.99933
ORdate_year2015 NA NA 0.000e+00 NA NA
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 1.358e+00 7.364e-01 0.81912 2.251
Age 1.038e+00 9.637e-01 1.00976 1.066
Gendermale 1.072e+00 9.330e-01 0.64399 1.784
ORdate_year2003 7.767e-01 1.288e+00 0.24621 2.450
ORdate_year2004 6.536e-01 1.530e+00 0.21055 2.029
ORdate_year2005 1.067e+00 9.376e-01 0.37138 3.063
ORdate_year2006 9.406e-01 1.063e+00 0.31905 2.773
ORdate_year2007 4.080e-01 2.451e+00 0.11789 1.412
ORdate_year2008 6.605e-01 1.514e+00 0.20076 2.173
ORdate_year2009 2.333e-08 4.286e+07 0.00000 Inf
ORdate_year2010 6.101e-01 1.639e+00 0.18374 2.026
ORdate_year2011 4.240e-01 2.358e+00 0.12185 1.476
ORdate_year2012 8.295e-01 1.205e+00 0.26939 2.555
ORdate_year2013 4.364e-01 2.291e+00 0.05061 3.763
ORdate_year2014 2.349e-08 4.257e+07 0.00000 Inf
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Concordance= 0.658 (se = 0.031 )
Likelihood ratio test= 27.64 on 15 df, p=0.02
Wald test = 7.14 on 15 df, p=1
Score (logrank) test = 21.29 on 15 df, p=0.1
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.305917
Standard error............: 0.257882
Odds ratio (effect size)..: 1.358
Lower 95% CI..............: 0.819
Upper 95% CI..............: 2.251
T-value...................: 1.186266
P-value...................: 0.2355172
Sample size in model......: 1186
Number of events..........: 73
> processing [MCP1_pg_ml_2015_rank]; 2 out of 3 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 1187, number of events= 74
(1236 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 8.025e-02 1.084e+00 2.608e-01 0.308 0.75826
Age 3.618e-02 1.037e+00 1.384e-02 2.614 0.00894 **
Gendermale 9.547e-02 1.100e+00 2.604e-01 0.367 0.71390
ORdate_year2003 -2.679e-01 7.650e-01 5.867e-01 -0.457 0.64799
ORdate_year2004 -5.056e-01 6.032e-01 5.778e-01 -0.875 0.38162
ORdate_year2005 -1.701e-05 1.000e+00 5.384e-01 0.000 0.99997
ORdate_year2006 -1.243e-01 8.831e-01 5.489e-01 -0.226 0.82088
ORdate_year2007 -8.959e-01 4.082e-01 6.334e-01 -1.414 0.15728
ORdate_year2008 -4.022e-01 6.689e-01 6.082e-01 -0.661 0.50844
ORdate_year2009 -1.756e+01 2.362e-08 2.047e+03 -0.009 0.99315
ORdate_year2010 -4.281e-01 6.518e-01 6.111e-01 -0.700 0.48366
ORdate_year2011 -8.265e-01 4.376e-01 6.377e-01 -1.296 0.19496
ORdate_year2012 -5.342e-02 9.480e-01 5.650e-01 -0.095 0.92468
ORdate_year2013 -7.674e-01 4.642e-01 1.100e+00 -0.698 0.48529
ORdate_year2014 -1.747e+01 2.591e-08 2.081e+04 -0.001 0.99933
ORdate_year2015 NA NA 0.000e+00 NA NA
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.084e+00 9.229e-01 0.64998 1.806
Age 1.037e+00 9.645e-01 1.00910 1.065
Gendermale 1.100e+00 9.089e-01 0.66041 1.833
ORdate_year2003 7.650e-01 1.307e+00 0.24224 2.416
ORdate_year2004 6.032e-01 1.658e+00 0.19435 1.872
ORdate_year2005 1.000e+00 1.000e+00 0.34813 2.872
ORdate_year2006 8.831e-01 1.132e+00 0.30119 2.590
ORdate_year2007 4.082e-01 2.449e+00 0.11796 1.413
ORdate_year2008 6.689e-01 1.495e+00 0.20309 2.203
ORdate_year2009 2.362e-08 4.234e+07 0.00000 Inf
ORdate_year2010 6.518e-01 1.534e+00 0.19674 2.159
ORdate_year2011 4.376e-01 2.285e+00 0.12538 1.527
ORdate_year2012 9.480e-01 1.055e+00 0.31323 2.869
ORdate_year2013 4.642e-01 2.154e+00 0.05379 4.007
ORdate_year2014 2.591e-08 3.860e+07 0.00000 Inf
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Concordance= 0.651 (se = 0.03 )
Likelihood ratio test= 27.21 on 15 df, p=0.03
Wald test = 6.34 on 15 df, p=1
Score (logrank) test = 20.9 on 15 df, p=0.1
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: 0.080251
Standard error............: 0.260754
Odds ratio (effect size)..: 1.084
Lower 95% CI..............: 0.65
Upper 95% CI..............: 1.806
T-value...................: 0.307764
P-value...................: 0.7582618
Sample size in model......: 1187
Number of events..........: 74
> processing [MCP1_rank]; 3 out of 3 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 549, number of events= 36
(1874 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -0.382334 0.682267 0.351409 -1.088 0.277
Age 0.006951 1.006975 0.019912 0.349 0.727
Gendermale 0.346148 1.413612 0.404465 0.856 0.392
ORdate_year2003 -0.384209 0.680989 0.589009 -0.652 0.514
ORdate_year2004 -0.284457 0.752423 0.554563 -0.513 0.608
ORdate_year2005 -0.219538 0.802890 0.548005 -0.401 0.689
ORdate_year2006 -0.200554 0.818277 0.856136 -0.234 0.815
ORdate_year2007 NA NA 0.000000 NA NA
ORdate_year2008 NA NA 0.000000 NA NA
ORdate_year2009 NA NA 0.000000 NA NA
ORdate_year2010 NA NA 0.000000 NA NA
ORdate_year2011 NA NA 0.000000 NA NA
ORdate_year2012 NA NA 0.000000 NA NA
ORdate_year2013 NA NA 0.000000 NA NA
ORdate_year2014 NA NA 0.000000 NA NA
ORdate_year2015 NA NA 0.000000 NA NA
ORdate_year2016 NA NA 0.000000 NA NA
ORdate_year2017 NA NA 0.000000 NA NA
ORdate_year2018 NA NA 0.000000 NA NA
ORdate_year2019 NA NA 0.000000 NA NA
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 0.6823 1.4657 0.3426 1.359
Age 1.0070 0.9931 0.9684 1.047
Gendermale 1.4136 0.7074 0.6398 3.123
ORdate_year2003 0.6810 1.4685 0.2147 2.160
ORdate_year2004 0.7524 1.3290 0.2538 2.231
ORdate_year2005 0.8029 1.2455 0.2743 2.350
ORdate_year2006 0.8183 1.2221 0.1528 4.382
ORdate_year2007 NA NA NA NA
ORdate_year2008 NA NA NA NA
ORdate_year2009 NA NA NA NA
ORdate_year2010 NA NA NA NA
ORdate_year2011 NA NA NA NA
ORdate_year2012 NA NA NA NA
ORdate_year2013 NA NA NA NA
ORdate_year2014 NA NA NA NA
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Concordance= 0.562 (se = 0.042 )
Likelihood ratio test= 2.39 on 7 df, p=0.9
Wald test = 2.32 on 7 df, p=0.9
Score (logrank) test = 2.33 on 7 df, p=0.9
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_rank
Effect size...............: -0.382334
Standard error............: 0.351409
Odds ratio (effect size)..: 0.682
Lower 95% CI..............: 0.343
Upper 95% CI..............: 1.359
T-value...................: -1.088003
P-value...................: 0.2765937
Sample size in model......: 549
Number of events..........: 36
* Analyzing the effect of plaque proteins on [epcoronary.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 3 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34102,0.00105) [ 0.00105,3.34102]
599 599
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 1186, number of events= 91
(1237 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] -0.21553 0.80611 0.22795 -0.946 0.3444
Age 0.01118 1.01125 0.01240 0.901 0.3673
Gendermale 0.67583 1.96567 0.27042 2.499 0.0124 *
ORdate_year2003 -0.06077 0.94104 0.44392 -0.137 0.8911
ORdate_year2004 -0.94266 0.38959 0.49032 -1.923 0.0545 .
ORdate_year2005 -0.77760 0.45951 0.48894 -1.590 0.1118
ORdate_year2006 -0.73396 0.48000 0.48769 -1.505 0.1323
ORdate_year2007 -1.13892 0.32016 0.54048 -2.107 0.0351 *
ORdate_year2008 -0.32424 0.72308 0.48704 -0.666 0.5056
ORdate_year2009 -1.06744 0.34389 0.57068 -1.870 0.0614 .
ORdate_year2010 -1.12460 0.32478 0.61751 -1.821 0.0686 .
ORdate_year2011 -1.17692 0.30823 0.57416 -2.050 0.0404 *
ORdate_year2012 -0.26436 0.76770 0.47791 -0.553 0.5802
ORdate_year2013 -0.33184 0.71761 0.79690 -0.416 0.6771
ORdate_year2014 2.10877 8.23807 1.07107 1.969 0.0490 *
ORdate_year2015 NA NA 0.00000 NA NA
ORdate_year2016 NA NA 0.00000 NA NA
ORdate_year2017 NA NA 0.00000 NA NA
ORdate_year2018 NA NA 0.00000 NA NA
ORdate_year2019 NA NA 0.00000 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 0.8061 1.2405 0.51566 1.2602
Age 1.0112 0.9889 0.98696 1.0361
Gendermale 1.9657 0.5087 1.15699 3.3396
ORdate_year2003 0.9410 1.0627 0.39422 2.2463
ORdate_year2004 0.3896 2.5668 0.14902 1.0185
ORdate_year2005 0.4595 2.1762 0.17624 1.1981
ORdate_year2006 0.4800 2.0833 0.18455 1.2485
ORdate_year2007 0.3202 3.1234 0.11100 0.9235
ORdate_year2008 0.7231 1.3830 0.27837 1.8783
ORdate_year2009 0.3439 2.9079 0.11237 1.0524
ORdate_year2010 0.3248 3.0790 0.09682 1.0895
ORdate_year2011 0.3082 3.2444 0.10003 0.9497
ORdate_year2012 0.7677 1.3026 0.30088 1.9588
ORdate_year2013 0.7176 1.3935 0.15051 3.4214
ORdate_year2014 8.2381 0.1214 1.00955 67.2237
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Concordance= 0.642 (se = 0.031 )
Likelihood ratio test= 27.09 on 15 df, p=0.03
Wald test = 29.47 on 15 df, p=0.01
Score (logrank) test = 37.33 on 15 df, p=0.001
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: -0.215531
Standard error............: 0.227948
Odds ratio (effect size)..: 0.806
Lower 95% CI..............: 0.516
Upper 95% CI..............: 1.26
T-value...................: -0.945527
P-value...................: 0.3443898
Sample size in model......: 1186
Number of events..........: 91
> processing [MCP1_pg_ml_2015_rank]; 2 out of 3 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 1187, number of events= 91
(1236 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 0.06316 1.06519 0.23409 0.270 0.7873
Age 0.01131 1.01137 0.01240 0.912 0.3619
Gendermale 0.64893 1.91350 0.27179 2.388 0.0170 *
ORdate_year2003 -0.05230 0.94905 0.44420 -0.118 0.9063
ORdate_year2004 -0.85654 0.42463 0.49277 -1.738 0.0822 .
ORdate_year2005 -0.70863 0.49232 0.49021 -1.446 0.1483
ORdate_year2006 -0.69400 0.49957 0.48622 -1.427 0.1535
ORdate_year2007 -1.14851 0.31711 0.54050 -2.125 0.0336 *
ORdate_year2008 -0.35417 0.70176 0.48738 -0.727 0.4674
ORdate_year2009 -1.10011 0.33283 0.57243 -1.922 0.0546 .
ORdate_year2010 -1.20428 0.29991 0.61538 -1.957 0.0504 .
ORdate_year2011 -1.24303 0.28851 0.57491 -2.162 0.0306 *
ORdate_year2012 -0.34161 0.71063 0.48030 -0.711 0.4769
ORdate_year2013 -0.42740 0.65220 0.79633 -0.537 0.5915
ORdate_year2014 1.97047 7.17408 1.06996 1.842 0.0655 .
ORdate_year2015 NA NA 0.00000 NA NA
ORdate_year2016 NA NA 0.00000 NA NA
ORdate_year2017 NA NA 0.00000 NA NA
ORdate_year2018 NA NA 0.00000 NA NA
ORdate_year2019 NA NA 0.00000 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.0652 0.9388 0.67325 1.6853
Age 1.0114 0.9888 0.98709 1.0363
Gendermale 1.9135 0.5226 1.12326 3.2597
ORdate_year2003 0.9490 1.0537 0.39736 2.2667
ORdate_year2004 0.4246 2.3550 0.16164 1.1155
ORdate_year2005 0.4923 2.0312 0.18835 1.2868
ORdate_year2006 0.4996 2.0017 0.19263 1.2956
ORdate_year2007 0.3171 3.1535 0.10993 0.9147
ORdate_year2008 0.7018 1.4250 0.26998 1.8241
ORdate_year2009 0.3328 3.0045 0.10839 1.0221
ORdate_year2010 0.2999 3.3344 0.08978 1.0018
ORdate_year2011 0.2885 3.4661 0.09350 0.8903
ORdate_year2012 0.7106 1.4072 0.27721 1.8217
ORdate_year2013 0.6522 1.5333 0.13695 3.1061
ORdate_year2014 7.1741 0.1394 0.88107 58.4147
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Concordance= 0.639 (se = 0.031 )
Likelihood ratio test= 26.15 on 15 df, p=0.04
Wald test = 28.69 on 15 df, p=0.02
Score (logrank) test = 36.58 on 15 df, p=0.001
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: 0.063156
Standard error............: 0.234085
Odds ratio (effect size)..: 1.065
Lower 95% CI..............: 0.673
Upper 95% CI..............: 1.685
T-value...................: 0.2698
P-value...................: 0.7873138
Sample size in model......: 1187
Number of events..........: 91
> processing [MCP1_rank]; 3 out of 3 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 549, number of events= 46
(1874 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 0.19648 1.21711 0.31418 0.625 0.5317
Age 0.03677 1.03745 0.01891 1.944 0.0519 .
Gendermale 0.92058 2.51074 0.44004 2.092 0.0364 *
ORdate_year2003 -0.17409 0.84022 0.43219 -0.403 0.6871
ORdate_year2004 -0.81070 0.44455 0.47567 -1.704 0.0883 .
ORdate_year2005 -0.67062 0.51139 0.45675 -1.468 0.1420
ORdate_year2006 -0.70564 0.49379 0.79859 -0.884 0.3769
ORdate_year2007 NA NA 0.00000 NA NA
ORdate_year2008 NA NA 0.00000 NA NA
ORdate_year2009 NA NA 0.00000 NA NA
ORdate_year2010 NA NA 0.00000 NA NA
ORdate_year2011 NA NA 0.00000 NA NA
ORdate_year2012 NA NA 0.00000 NA NA
ORdate_year2013 NA NA 0.00000 NA NA
ORdate_year2014 NA NA 0.00000 NA NA
ORdate_year2015 NA NA 0.00000 NA NA
ORdate_year2016 NA NA 0.00000 NA NA
ORdate_year2017 NA NA 0.00000 NA NA
ORdate_year2018 NA NA 0.00000 NA NA
ORdate_year2019 NA NA 0.00000 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 1.2171 0.8216 0.6575 2.253
Age 1.0374 0.9639 0.9997 1.077
Gendermale 2.5107 0.3983 1.0598 5.948
ORdate_year2003 0.8402 1.1902 0.3602 1.960
ORdate_year2004 0.4445 2.2495 0.1750 1.129
ORdate_year2005 0.5114 1.9555 0.2089 1.252
ORdate_year2006 0.4938 2.0251 0.1032 2.362
ORdate_year2007 NA NA NA NA
ORdate_year2008 NA NA NA NA
ORdate_year2009 NA NA NA NA
ORdate_year2010 NA NA NA NA
ORdate_year2011 NA NA NA NA
ORdate_year2012 NA NA NA NA
ORdate_year2013 NA NA NA NA
ORdate_year2014 NA NA NA NA
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Concordance= 0.66 (se = 0.037 )
Likelihood ratio test= 15.3 on 7 df, p=0.03
Wald test = 14.05 on 7 df, p=0.05
Score (logrank) test = 14.66 on 7 df, p=0.04
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_rank
Effect size...............: 0.196482
Standard error............: 0.314184
Odds ratio (effect size)..: 1.217
Lower 95% CI..............: 0.657
Upper 95% CI..............: 2.253
T-value...................: 0.625374
P-value...................: 0.5317255
Sample size in model......: 549
Number of events..........: 46
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 3 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34102,0.00105) [ 0.00105,3.34102]
599 599
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 1186, number of events= 45
(1237 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] -2.904e-02 9.714e-01 3.235e-01 -0.090 0.9285
Age 9.205e-02 1.096e+00 2.033e-02 4.527 5.99e-06 ***
Gendermale 8.922e-01 2.440e+00 4.135e-01 2.158 0.0309 *
ORdate_year2003 -2.845e-01 7.524e-01 6.713e-01 -0.424 0.6718
ORdate_year2004 -8.294e-01 4.363e-01 6.800e-01 -1.220 0.2225
ORdate_year2005 -7.072e-01 4.930e-01 6.772e-01 -1.044 0.2963
ORdate_year2006 -7.109e-01 4.912e-01 6.751e-01 -1.053 0.2923
ORdate_year2007 -1.280e+00 2.782e-01 7.662e-01 -1.670 0.0949 .
ORdate_year2008 -6.812e-01 5.060e-01 7.118e-01 -0.957 0.3386
ORdate_year2009 -1.048e+00 3.506e-01 7.670e-01 -1.366 0.1718
ORdate_year2010 -1.042e+00 3.527e-01 7.771e-01 -1.341 0.1799
ORdate_year2011 -1.754e+00 1.731e-01 8.747e-01 -2.005 0.0450 *
ORdate_year2012 -6.007e-01 5.484e-01 6.804e-01 -0.883 0.3773
ORdate_year2013 -6.508e-01 5.216e-01 1.127e+00 -0.577 0.5638
ORdate_year2014 -1.368e+01 1.146e-06 3.890e+03 -0.004 0.9972
ORdate_year2015 NA NA 0.000e+00 NA NA
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 9.714e-01 1.029e+00 0.51525 1.8313
Age 1.096e+00 9.121e-01 1.05358 1.1410
Gendermale 2.440e+00 4.098e-01 1.08521 5.4878
ORdate_year2003 7.524e-01 1.329e+00 0.20186 2.8046
ORdate_year2004 4.363e-01 2.292e+00 0.11507 1.6542
ORdate_year2005 4.930e-01 2.028e+00 0.13073 1.8591
ORdate_year2006 4.912e-01 2.036e+00 0.13079 1.8446
ORdate_year2007 2.782e-01 3.595e+00 0.06196 1.2487
ORdate_year2008 5.060e-01 1.976e+00 0.12539 2.0420
ORdate_year2009 3.506e-01 2.852e+00 0.07797 1.5767
ORdate_year2010 3.527e-01 2.835e+00 0.07691 1.6175
ORdate_year2011 1.731e-01 5.776e+00 0.03118 0.9614
ORdate_year2012 5.484e-01 1.823e+00 0.14453 2.0812
ORdate_year2013 5.216e-01 1.917e+00 0.05726 4.7526
ORdate_year2014 1.146e-06 8.725e+05 0.00000 Inf
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Concordance= 0.732 (se = 0.037 )
Likelihood ratio test= 33.9 on 15 df, p=0.004
Wald test = 29.06 on 15 df, p=0.02
Score (logrank) test = 30.01 on 15 df, p=0.01
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: -0.029035
Standard error............: 0.323507
Odds ratio (effect size)..: 0.971
Lower 95% CI..............: 0.515
Upper 95% CI..............: 1.831
T-value...................: -0.089752
P-value...................: 0.9284844
Sample size in model......: 1186
Number of events..........: 45
> processing [MCP1_pg_ml_2015_rank]; 2 out of 3 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 1187, number of events= 45
(1236 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] -2.013e-01 8.177e-01 3.330e-01 -0.604 0.5455
Age 9.257e-02 1.097e+00 2.031e-02 4.558 5.17e-06 ***
Gendermale 9.156e-01 2.498e+00 4.152e-01 2.205 0.0274 *
ORdate_year2003 -3.002e-01 7.406e-01 6.718e-01 -0.447 0.6550
ORdate_year2004 -8.848e-01 4.128e-01 6.796e-01 -1.302 0.1930
ORdate_year2005 -7.587e-01 4.683e-01 6.786e-01 -1.118 0.2636
ORdate_year2006 -7.048e-01 4.942e-01 6.728e-01 -1.047 0.2949
ORdate_year2007 -1.269e+00 2.811e-01 7.661e-01 -1.657 0.0976 .
ORdate_year2008 -6.525e-01 5.207e-01 7.124e-01 -0.916 0.3597
ORdate_year2009 -1.005e+00 3.659e-01 7.703e-01 -1.305 0.1918
ORdate_year2010 -1.003e+00 3.667e-01 7.748e-01 -1.295 0.1954
ORdate_year2011 -1.709e+00 1.810e-01 8.762e-01 -1.950 0.0511 .
ORdate_year2012 -5.593e-01 5.716e-01 6.857e-01 -0.816 0.4147
ORdate_year2013 -5.827e-01 5.584e-01 1.128e+00 -0.516 0.6055
ORdate_year2014 -1.359e+01 1.251e-06 3.895e+03 -0.003 0.9972
ORdate_year2015 NA NA 0.000e+00 NA NA
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 8.177e-01 1.223e+00 0.42575 1.570
Age 1.097e+00 9.116e-01 1.05418 1.142
Gendermale 2.498e+00 4.003e-01 1.10717 5.638
ORdate_year2003 7.406e-01 1.350e+00 0.19849 2.764
ORdate_year2004 4.128e-01 2.422e+00 0.10895 1.564
ORdate_year2005 4.683e-01 2.135e+00 0.12384 1.771
ORdate_year2006 4.942e-01 2.023e+00 0.13220 1.848
ORdate_year2007 2.811e-01 3.558e+00 0.06262 1.262
ORdate_year2008 5.207e-01 1.920e+00 0.12889 2.104
ORdate_year2009 3.659e-01 2.733e+00 0.08085 1.656
ORdate_year2010 3.667e-01 2.727e+00 0.08033 1.674
ORdate_year2011 1.810e-01 5.524e+00 0.03250 1.008
ORdate_year2012 5.716e-01 1.750e+00 0.14907 2.192
ORdate_year2013 5.584e-01 1.791e+00 0.06116 5.098
ORdate_year2014 1.251e-06 7.992e+05 0.00000 Inf
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Concordance= 0.733 (se = 0.037 )
Likelihood ratio test= 34.22 on 15 df, p=0.003
Wald test = 29.53 on 15 df, p=0.01
Score (logrank) test = 30.23 on 15 df, p=0.01
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: -0.201269
Standard error............: 0.332978
Odds ratio (effect size)..: 0.818
Lower 95% CI..............: 0.426
Upper 95% CI..............: 1.57
T-value...................: -0.604452
P-value...................: 0.5455431
Sample size in model......: 1187
Number of events..........: 45
> processing [MCP1_rank]; 3 out of 3 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 549, number of events= 26
(1874 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -0.09793 0.90671 0.41602 -0.235 0.8139
Age 0.05263 1.05404 0.02576 2.043 0.0410 *
Gendermale 1.06154 2.89081 0.61643 1.722 0.0851 .
ORdate_year2003 -1.05582 0.34791 0.65095 -1.622 0.1048
ORdate_year2004 -0.83426 0.43420 0.58109 -1.436 0.1511
ORdate_year2005 -0.74400 0.47521 0.56878 -1.308 0.1909
ORdate_year2006 -0.42705 0.65243 0.84244 -0.507 0.6122
ORdate_year2007 NA NA 0.00000 NA NA
ORdate_year2008 NA NA 0.00000 NA NA
ORdate_year2009 NA NA 0.00000 NA NA
ORdate_year2010 NA NA 0.00000 NA NA
ORdate_year2011 NA NA 0.00000 NA NA
ORdate_year2012 NA NA 0.00000 NA NA
ORdate_year2013 NA NA 0.00000 NA NA
ORdate_year2014 NA NA 0.00000 NA NA
ORdate_year2015 NA NA 0.00000 NA NA
ORdate_year2016 NA NA 0.00000 NA NA
ORdate_year2017 NA NA 0.00000 NA NA
ORdate_year2018 NA NA 0.00000 NA NA
ORdate_year2019 NA NA 0.00000 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 0.9067 1.1029 0.40119 2.049
Age 1.0540 0.9487 1.00215 1.109
Gendermale 2.8908 0.3459 0.86360 9.677
ORdate_year2003 0.3479 2.8743 0.09713 1.246
ORdate_year2004 0.4342 2.3031 0.13901 1.356
ORdate_year2005 0.4752 2.1043 0.15586 1.449
ORdate_year2006 0.6524 1.5327 0.12516 3.401
ORdate_year2007 NA NA NA NA
ORdate_year2008 NA NA NA NA
ORdate_year2009 NA NA NA NA
ORdate_year2010 NA NA NA NA
ORdate_year2011 NA NA NA NA
ORdate_year2012 NA NA NA NA
ORdate_year2013 NA NA NA NA
ORdate_year2014 NA NA NA NA
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Concordance= 0.692 (se = 0.05 )
Likelihood ratio test= 12.26 on 7 df, p=0.09
Wald test = 11.31 on 7 df, p=0.1
Score (logrank) test = 11.89 on 7 df, p=0.1
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_rank
Effect size...............: -0.097933
Standard error............: 0.416021
Odds ratio (effect size)..: 0.907
Lower 95% CI..............: 0.401
Upper 95% CI..............: 2.049
T-value...................: -0.235405
P-value...................: 0.8138946
Sample size in model......: 549
Number of events..........: 26 
cat("- Edit the column names...\n")- Edit the column names...colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")- Correct the variable types...COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")- Writing results to Excel-file...head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")- Removing intermediate files...#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
#rm(head.style)
MODEL 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
eptime = times[i]
ep = endpoints[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
#as.integer(TEMP.DF[,ep] == "Excluded")
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
# ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [years]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}* Analyzing the effect of plaque proteins on [epmajor.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 3 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34102,0.00105) [ 0.00105,3.34102]
599 599
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1029, number of events= 115
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 2.569e-01 1.293e+00 2.050e-01 1.253 0.21012
Age 3.470e-02 1.035e+00 1.316e-02 2.637 0.00836 **
Gendermale 3.732e-01 1.452e+00 2.288e-01 1.631 0.10282
ORdate_year2003 -3.123e-01 7.318e-01 4.535e-01 -0.689 0.49105
ORdate_year2004 -4.291e-01 6.511e-01 4.410e-01 -0.973 0.33054
ORdate_year2005 1.479e-01 1.159e+00 4.254e-01 0.348 0.72803
ORdate_year2006 3.386e-02 1.034e+00 4.323e-01 0.078 0.93756
ORdate_year2007 -7.204e-01 4.866e-01 5.153e-01 -1.398 0.16211
ORdate_year2008 -1.105e-01 8.954e-01 4.827e-01 -0.229 0.81891
ORdate_year2009 -9.545e-01 3.850e-01 5.681e-01 -1.680 0.09294 .
ORdate_year2010 -7.206e-01 4.864e-01 5.162e-01 -1.396 0.16274
ORdate_year2011 -8.705e-01 4.188e-01 5.383e-01 -1.617 0.10586
ORdate_year2012 4.796e-01 1.615e+00 4.415e-01 1.086 0.27734
ORdate_year2013 1.570e-01 1.170e+00 8.886e-01 0.177 0.85979
ORdate_year2014 NA NA 0.000e+00 NA NA
ORdate_year2015 NA NA 0.000e+00 NA NA
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
Hypertension.compositeno -4.566e-01 6.334e-01 3.618e-01 -1.262 0.20696
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -2.722e-02 9.731e-01 2.279e-01 -0.119 0.90492
SmokerStatusEx-smoker -4.569e-01 6.332e-01 2.105e-01 -2.170 0.03000 *
SmokerStatusNever smoked -8.064e-01 4.465e-01 3.437e-01 -2.346 0.01895 *
Med.Statin.LLDno 2.661e-01 1.305e+00 2.211e-01 1.203 0.22883
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 4.593e-01 1.583e+00 2.668e-01 1.721 0.08518 .
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.995e-02 9.802e-01 5.047e-03 -3.953 7.72e-05 ***
BMI 5.567e-02 1.057e+00 2.581e-02 2.157 0.03102 *
MedHx_CVDyes 5.472e-01 1.728e+00 2.247e-01 2.435 0.01490 *
stenose0-49% -1.505e+01 2.896e-07 2.393e+03 -0.006 0.99498
stenose50-70% -6.967e-02 9.327e-01 9.527e-01 -0.073 0.94170
stenose70-90% 3.744e-01 1.454e+00 8.404e-01 0.445 0.65597
stenose90-99% 4.140e-01 1.513e+00 8.413e-01 0.492 0.62269
stenose100% (Occlusion) 6.412e-01 1.899e+00 1.323e+00 0.484 0.62805
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.551e+01 1.845e-07 3.067e+03 -0.005 0.99597
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 1.293e+00 7.734e-01 0.8651 1.9324
Age 1.035e+00 9.659e-01 1.0089 1.0624
Gendermale 1.452e+00 6.885e-01 0.9276 2.2743
ORdate_year2003 7.318e-01 1.367e+00 0.3008 1.7799
ORdate_year2004 6.511e-01 1.536e+00 0.2744 1.5453
ORdate_year2005 1.159e+00 8.625e-01 0.5036 2.6693
ORdate_year2006 1.034e+00 9.667e-01 0.4434 2.4135
ORdate_year2007 4.866e-01 2.055e+00 0.1772 1.3359
ORdate_year2008 8.954e-01 1.117e+00 0.3477 2.3059
ORdate_year2009 3.850e-01 2.597e+00 0.1265 1.1723
ORdate_year2010 4.864e-01 2.056e+00 0.1768 1.3380
ORdate_year2011 4.188e-01 2.388e+00 0.1458 1.2027
ORdate_year2012 1.615e+00 6.190e-01 0.6800 3.8379
ORdate_year2013 1.170e+00 8.547e-01 0.2050 6.6765
ORdate_year2014 NA NA NA NA
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Hypertension.compositeno 6.334e-01 1.579e+00 0.3117 1.2873
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.731e-01 1.028e+00 0.6226 1.5211
SmokerStatusEx-smoker 6.332e-01 1.579e+00 0.4191 0.9567
SmokerStatusNever smoked 4.465e-01 2.240e+00 0.2276 0.8756
Med.Statin.LLDno 1.305e+00 7.664e-01 0.8459 2.0127
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.583e+00 6.317e-01 0.9383 2.6705
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.802e-01 1.020e+00 0.9706 0.9900
BMI 1.057e+00 9.459e-01 1.0051 1.1121
MedHx_CVDyes 1.728e+00 5.786e-01 1.1126 2.6850
stenose0-49% 2.896e-07 3.453e+06 0.0000 Inf
stenose50-70% 9.327e-01 1.072e+00 0.1441 6.0352
stenose70-90% 1.454e+00 6.877e-01 0.2801 7.5499
stenose90-99% 1.513e+00 6.610e-01 0.2908 7.8685
stenose100% (Occlusion) 1.899e+00 5.267e-01 0.1419 25.4074
stenoseNA NA NA NA NA
stenose50-99% 1.845e-07 5.421e+06 0.0000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.73 (se = 0.022 )
Likelihood ratio test= 83.47 on 29 df, p=4e-07
Wald test = 76.17 on 29 df, p=4e-06
Score (logrank) test = 81.79 on 29 df, p=6e-07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.256929
Standard error............: 0.205012
Odds ratio (effect size)..: 1.293
Lower 95% CI..............: 0.865
Upper 95% CI..............: 1.932
T-value...................: 1.25324
P-value...................: 0.2101183
Sample size in model......: 1029
Number of events..........: 115
> processing [MCP1_pg_ml_2015_rank]; 2 out of 3 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1029, number of events= 115
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 2.012e-01 1.223e+00 2.069e-01 0.973 0.330705
Age 3.415e-02 1.035e+00 1.313e-02 2.601 0.009303 **
Gendermale 3.655e-01 1.441e+00 2.292e-01 1.595 0.110708
ORdate_year2003 -3.039e-01 7.379e-01 4.536e-01 -0.670 0.502841
ORdate_year2004 -4.387e-01 6.449e-01 4.424e-01 -0.992 0.321301
ORdate_year2005 1.361e-01 1.146e+00 4.261e-01 0.319 0.749429
ORdate_year2006 -1.227e-02 9.878e-01 4.298e-01 -0.029 0.977232
ORdate_year2007 -7.093e-01 4.920e-01 5.152e-01 -1.377 0.168622
ORdate_year2008 -1.137e-01 8.925e-01 4.832e-01 -0.235 0.814010
ORdate_year2009 -9.736e-01 3.777e-01 5.700e-01 -1.708 0.087621 .
ORdate_year2010 -6.952e-01 4.990e-01 5.156e-01 -1.348 0.177531
ORdate_year2011 -8.708e-01 4.186e-01 5.396e-01 -1.614 0.106566
ORdate_year2012 4.741e-01 1.607e+00 4.428e-01 1.071 0.284300
ORdate_year2013 1.914e-01 1.211e+00 8.929e-01 0.214 0.830288
ORdate_year2014 NA NA 0.000e+00 NA NA
ORdate_year2015 NA NA 0.000e+00 NA NA
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
Hypertension.compositeno -4.501e-01 6.375e-01 3.618e-01 -1.244 0.213507
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -4.152e-02 9.593e-01 2.278e-01 -0.182 0.855335
SmokerStatusEx-smoker -4.579e-01 6.326e-01 2.106e-01 -2.175 0.029665 *
SmokerStatusNever smoked -8.149e-01 4.427e-01 3.443e-01 -2.367 0.017952 *
Med.Statin.LLDno 2.621e-01 1.300e+00 2.219e-01 1.181 0.237625
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 4.615e-01 1.586e+00 2.670e-01 1.728 0.083925 .
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.961e-02 9.806e-01 5.047e-03 -3.884 0.000103 ***
BMI 5.542e-02 1.057e+00 2.563e-02 2.163 0.030559 *
MedHx_CVDyes 5.571e-01 1.746e+00 2.247e-01 2.479 0.013183 *
stenose0-49% -1.513e+01 2.674e-07 2.373e+03 -0.006 0.994911
stenose50-70% -4.756e-02 9.536e-01 9.559e-01 -0.050 0.960316
stenose70-90% 3.692e-01 1.447e+00 8.443e-01 0.437 0.661889
stenose90-99% 4.096e-01 1.506e+00 8.454e-01 0.484 0.628071
stenose100% (Occlusion) 5.581e-01 1.747e+00 1.324e+00 0.422 0.673360
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.550e+01 1.858e-07 3.063e+03 -0.005 0.995963
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.223e+00 8.177e-01 0.8153 1.8344
Age 1.035e+00 9.664e-01 1.0085 1.0617
Gendermale 1.441e+00 6.938e-01 0.9198 2.2584
ORdate_year2003 7.379e-01 1.355e+00 0.3033 1.7951
ORdate_year2004 6.449e-01 1.551e+00 0.2710 1.5346
ORdate_year2005 1.146e+00 8.728e-01 0.4970 2.6414
ORdate_year2006 9.878e-01 1.012e+00 0.4254 2.2937
ORdate_year2007 4.920e-01 2.033e+00 0.1792 1.3506
ORdate_year2008 8.925e-01 1.120e+00 0.3462 2.3012
ORdate_year2009 3.777e-01 2.647e+00 0.1236 1.1544
ORdate_year2010 4.990e-01 2.004e+00 0.1817 1.3707
ORdate_year2011 4.186e-01 2.389e+00 0.1454 1.2054
ORdate_year2012 1.607e+00 6.224e-01 0.6745 3.8268
ORdate_year2013 1.211e+00 8.258e-01 0.2104 6.9684
ORdate_year2014 NA NA NA NA
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Hypertension.compositeno 6.375e-01 1.569e+00 0.3137 1.2957
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.593e-01 1.042e+00 0.6139 1.4991
SmokerStatusEx-smoker 6.326e-01 1.581e+00 0.4187 0.9558
SmokerStatusNever smoked 4.427e-01 2.259e+00 0.2254 0.8693
Med.Statin.LLDno 1.300e+00 7.695e-01 0.8412 2.0077
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.586e+00 6.303e-01 0.9400 2.6774
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.806e-01 1.020e+00 0.9709 0.9903
BMI 1.057e+00 9.461e-01 1.0052 1.1114
MedHx_CVDyes 1.746e+00 5.729e-01 1.1237 2.7117
stenose0-49% 2.674e-07 3.739e+06 0.0000 Inf
stenose50-70% 9.536e-01 1.049e+00 0.1465 6.2084
stenose70-90% 1.447e+00 6.913e-01 0.2765 7.5688
stenose90-99% 1.506e+00 6.639e-01 0.2872 7.8979
stenose100% (Occlusion) 1.747e+00 5.723e-01 0.1304 23.4104
stenoseNA NA NA NA NA
stenose50-99% 1.858e-07 5.382e+06 0.0000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.728 (se = 0.022 )
Likelihood ratio test= 82.84 on 29 df, p=4e-07
Wald test = 75.29 on 29 df, p=6e-06
Score (logrank) test = 81.04 on 29 df, p=8e-07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: 0.201234
Standard error............: 0.206883
Odds ratio (effect size)..: 1.223
Lower 95% CI..............: 0.815
Upper 95% CI..............: 1.834
T-value...................: 0.972695
P-value...................: 0.330705
Sample size in model......: 1029
Number of events..........: 115
> processing [MCP1_rank]; 3 out of 3 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 493, number of events= 61
(1930 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -4.150e-01 6.603e-01 2.782e-01 -1.492 0.1358
Age 2.986e-02 1.030e+00 1.813e-02 1.648 0.0995 .
Gendermale 7.973e-01 2.220e+00 3.701e-01 2.154 0.0312 *
ORdate_year2003 -8.489e-01 4.279e-01 4.375e-01 -1.940 0.0523 .
ORdate_year2004 -6.914e-01 5.009e-01 4.006e-01 -1.726 0.0844 .
ORdate_year2005 -6.347e-01 5.301e-01 4.001e-01 -1.586 0.1127
ORdate_year2006 1.724e-01 1.188e+00 5.631e-01 0.306 0.7594
ORdate_year2007 NA NA 0.000e+00 NA NA
ORdate_year2008 NA NA 0.000e+00 NA NA
ORdate_year2009 NA NA 0.000e+00 NA NA
ORdate_year2010 NA NA 0.000e+00 NA NA
ORdate_year2011 NA NA 0.000e+00 NA NA
ORdate_year2012 NA NA 0.000e+00 NA NA
ORdate_year2013 NA NA 0.000e+00 NA NA
ORdate_year2014 NA NA 0.000e+00 NA NA
ORdate_year2015 NA NA 0.000e+00 NA NA
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
Hypertension.compositeno -7.210e-01 4.863e-01 5.345e-01 -1.349 0.1774
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 6.302e-01 1.878e+00 2.971e-01 2.122 0.0339 *
SmokerStatusEx-smoker -6.687e-01 5.124e-01 2.916e-01 -2.293 0.0218 *
SmokerStatusNever smoked -3.377e-01 7.134e-01 4.351e-01 -0.776 0.4376
Med.Statin.LLDno 2.832e-01 1.327e+00 2.991e-01 0.947 0.3438
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno -1.674e-01 8.459e-01 4.616e-01 -0.363 0.7169
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -9.546e-03 9.905e-01 6.787e-03 -1.406 0.1596
BMI -7.853e-04 9.992e-01 3.558e-02 -0.022 0.9824
MedHx_CVDyes 5.402e-01 1.716e+00 3.083e-01 1.752 0.0798 .
stenose0-49% -1.563e+01 1.635e-07 2.223e+03 -0.007 0.9944
stenose50-70% -1.880e+00 1.526e-01 1.460e+00 -1.288 0.1979
stenose70-90% -8.716e-01 4.183e-01 1.064e+00 -0.819 0.4125
stenose90-99% -1.158e+00 3.142e-01 1.072e+00 -1.080 0.2802
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 6.603e-01 1.514e+00 0.382781 1.1391
Age 1.030e+00 9.706e-01 0.994353 1.0676
Gendermale 2.220e+00 4.505e-01 1.074555 4.5845
ORdate_year2003 4.279e-01 2.337e+00 0.181505 1.0087
ORdate_year2004 5.009e-01 1.996e+00 0.228421 1.0984
ORdate_year2005 5.301e-01 1.886e+00 0.241968 1.1614
ORdate_year2006 1.188e+00 8.416e-01 0.394084 3.5826
ORdate_year2007 NA NA NA NA
ORdate_year2008 NA NA NA NA
ORdate_year2009 NA NA NA NA
ORdate_year2010 NA NA NA NA
ORdate_year2011 NA NA NA NA
ORdate_year2012 NA NA NA NA
ORdate_year2013 NA NA NA NA
ORdate_year2014 NA NA NA NA
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Hypertension.compositeno 4.863e-01 2.056e+00 0.170561 1.3863
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.878e+00 5.325e-01 1.049173 3.3617
SmokerStatusEx-smoker 5.124e-01 1.952e+00 0.289302 0.9074
SmokerStatusNever smoked 7.134e-01 1.402e+00 0.304071 1.6737
Med.Statin.LLDno 1.327e+00 7.534e-01 0.738562 2.3854
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 8.459e-01 1.182e+00 0.342283 2.0904
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.905e-01 1.010e+00 0.977410 1.0038
BMI 9.992e-01 1.001e+00 0.931916 1.0714
MedHx_CVDyes 1.716e+00 5.826e-01 0.937922 3.1407
stenose0-49% 1.635e-07 6.117e+06 0.000000 Inf
stenose50-70% 1.526e-01 6.551e+00 0.008729 2.6691
stenose70-90% 4.183e-01 2.391e+00 0.052008 3.3638
stenose90-99% 3.142e-01 3.183e+00 0.038418 2.5695
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.723 (se = 0.029 )
Likelihood ratio test= 39.54 on 20 df, p=0.006
Wald test = 37.37 on 20 df, p=0.01
Score (logrank) test = 40.27 on 20 df, p=0.005
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_rank
Effect size...............: -0.415016
Standard error............: 0.278207
Odds ratio (effect size)..: 0.66
Lower 95% CI..............: 0.383
Upper 95% CI..............: 1.139
T-value...................: -1.491755
P-value...................: 0.1357633
Sample size in model......: 493
Number of events..........: 61
* Analyzing the effect of plaque proteins on [epstroke.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 3 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34102,0.00105) [ 0.00105,3.34102]
599 599
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1029, number of events= 59
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 3.620e-01 1.436e+00 2.925e-01 1.238 0.2159
Age 4.197e-02 1.043e+00 1.777e-02 2.362 0.0182 *
Gendermale -3.927e-03 9.961e-01 3.019e-01 -0.013 0.9896
ORdate_year2003 -2.929e-01 7.461e-01 6.777e-01 -0.432 0.6656
ORdate_year2004 -2.235e-01 7.997e-01 6.385e-01 -0.350 0.7263
ORdate_year2005 3.677e-01 1.444e+00 6.065e-01 0.606 0.5443
ORdate_year2006 2.102e-01 1.234e+00 6.253e-01 0.336 0.7367
ORdate_year2007 -5.299e-01 5.886e-01 7.227e-01 -0.733 0.4634
ORdate_year2008 -2.546e-01 7.752e-01 7.211e-01 -0.353 0.7240
ORdate_year2009 -1.842e+01 9.967e-09 3.865e+03 -0.005 0.9962
ORdate_year2010 -1.912e-01 8.260e-01 6.646e-01 -0.288 0.7737
ORdate_year2011 -6.589e-01 5.174e-01 7.258e-01 -0.908 0.3640
ORdate_year2012 2.664e-01 1.305e+00 6.659e-01 0.400 0.6891
ORdate_year2013 -1.879e+01 6.934e-09 9.638e+03 -0.002 0.9984
ORdate_year2014 NA NA 0.000e+00 NA NA
ORdate_year2015 NA NA 0.000e+00 NA NA
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
Hypertension.compositeno -2.380e-02 9.765e-01 4.227e-01 -0.056 0.9551
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -1.362e-02 9.865e-01 3.207e-01 -0.042 0.9661
SmokerStatusEx-smoker -8.409e-02 9.193e-01 2.949e-01 -0.285 0.7755
SmokerStatusNever smoked -9.352e-01 3.925e-01 5.236e-01 -1.786 0.0741 .
Med.Statin.LLDno 3.356e-01 1.399e+00 3.012e-01 1.114 0.2653
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 4.212e-01 1.524e+00 3.778e-01 1.115 0.2649
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -4.671e-03 9.953e-01 7.103e-03 -0.658 0.5108
BMI 7.942e-02 1.083e+00 3.350e-02 2.371 0.0177 *
MedHx_CVDyes 3.740e-01 1.454e+00 2.977e-01 1.256 0.2090
stenose0-49% -1.884e+01 6.548e-09 1.477e+04 -0.001 0.9990
stenose50-70% -5.096e-01 6.008e-01 1.244e+00 -0.410 0.6821
stenose70-90% -5.996e-01 5.490e-01 1.125e+00 -0.533 0.5941
stenose90-99% -6.204e-01 5.378e-01 1.137e+00 -0.546 0.5852
stenose100% (Occlusion) 3.498e-01 1.419e+00 1.530e+00 0.229 0.8191
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.913e+01 4.919e-09 1.932e+04 -0.001 0.9992
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 1.436e+00 6.963e-01 0.80952 2.548
Age 1.043e+00 9.589e-01 1.00717 1.080
Gendermale 9.961e-01 1.004e+00 0.55122 1.800
ORdate_year2003 7.461e-01 1.340e+00 0.19766 2.816
ORdate_year2004 7.997e-01 1.250e+00 0.22879 2.795
ORdate_year2005 1.444e+00 6.923e-01 0.43995 4.742
ORdate_year2006 1.234e+00 8.104e-01 0.36229 4.203
ORdate_year2007 5.886e-01 1.699e+00 0.14280 2.426
ORdate_year2008 7.752e-01 1.290e+00 0.18863 3.186
ORdate_year2009 9.967e-09 1.003e+08 0.00000 Inf
ORdate_year2010 8.260e-01 1.211e+00 0.22451 3.039
ORdate_year2011 5.174e-01 1.933e+00 0.12476 2.146
ORdate_year2012 1.305e+00 7.661e-01 0.35390 4.814
ORdate_year2013 6.934e-09 1.442e+08 0.00000 Inf
ORdate_year2014 NA NA NA NA
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Hypertension.compositeno 9.765e-01 1.024e+00 0.42646 2.236
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.865e-01 1.014e+00 0.52618 1.849
SmokerStatusEx-smoker 9.193e-01 1.088e+00 0.51579 1.639
SmokerStatusNever smoked 3.925e-01 2.548e+00 0.14066 1.095
Med.Statin.LLDno 1.399e+00 7.149e-01 0.77505 2.524
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.524e+00 6.563e-01 0.72669 3.195
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.953e-01 1.005e+00 0.98158 1.009
BMI 1.083e+00 9.237e-01 1.01386 1.156
MedHx_CVDyes 1.454e+00 6.880e-01 0.81099 2.605
stenose0-49% 6.548e-09 1.527e+08 0.00000 Inf
stenose50-70% 6.008e-01 1.665e+00 0.05246 6.879
stenose70-90% 5.490e-01 1.821e+00 0.06052 4.981
stenose90-99% 5.378e-01 1.860e+00 0.05795 4.990
stenose100% (Occlusion) 1.419e+00 7.048e-01 0.07077 28.445
stenoseNA NA NA NA NA
stenose50-99% 4.919e-09 2.033e+08 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.732 (se = 0.03 )
Likelihood ratio test= 42.34 on 29 df, p=0.05
Wald test = 20.23 on 29 df, p=0.9
Score (logrank) test = 36.86 on 29 df, p=0.1
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.361969
Standard error............: 0.292494
Odds ratio (effect size)..: 1.436
Lower 95% CI..............: 0.81
Upper 95% CI..............: 2.548
T-value...................: 1.237526
P-value...................: 0.215892
Sample size in model......: 1029
Number of events..........: 59
> processing [MCP1_pg_ml_2015_rank]; 2 out of 3 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1029, number of events= 59
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 2.373e-01 1.268e+00 2.920e-01 0.813 0.4164
Age 4.063e-02 1.041e+00 1.770e-02 2.295 0.0217 *
Gendermale -1.109e-02 9.890e-01 3.026e-01 -0.037 0.9708
ORdate_year2003 -3.011e-01 7.400e-01 6.781e-01 -0.444 0.6570
ORdate_year2004 -2.691e-01 7.641e-01 6.376e-01 -0.422 0.6730
ORdate_year2005 3.200e-01 1.377e+00 6.050e-01 0.529 0.5969
ORdate_year2006 1.451e-01 1.156e+00 6.232e-01 0.233 0.8158
ORdate_year2007 -5.208e-01 5.940e-01 7.231e-01 -0.720 0.4714
ORdate_year2008 -2.657e-01 7.666e-01 7.224e-01 -0.368 0.7130
ORdate_year2009 -1.845e+01 9.724e-09 3.878e+03 -0.005 0.9962
ORdate_year2010 -1.570e-01 8.547e-01 6.650e-01 -0.236 0.8134
ORdate_year2011 -6.557e-01 5.191e-01 7.294e-01 -0.899 0.3686
ORdate_year2012 2.655e-01 1.304e+00 6.703e-01 0.396 0.6921
ORdate_year2013 -1.876e+01 7.156e-09 9.574e+03 -0.002 0.9984
ORdate_year2014 NA NA 0.000e+00 NA NA
ORdate_year2015 NA NA 0.000e+00 NA NA
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
Hypertension.compositeno -2.238e-02 9.779e-01 4.242e-01 -0.053 0.9579
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -2.439e-02 9.759e-01 3.210e-01 -0.076 0.9394
SmokerStatusEx-smoker -7.426e-02 9.284e-01 2.949e-01 -0.252 0.8012
SmokerStatusNever smoked -9.206e-01 3.983e-01 5.230e-01 -1.760 0.0784 .
Med.Statin.LLDno 3.383e-01 1.403e+00 3.018e-01 1.121 0.2623
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 4.232e-01 1.527e+00 3.782e-01 1.119 0.2631
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -4.185e-03 9.958e-01 7.096e-03 -0.590 0.5553
BMI 7.790e-02 1.081e+00 3.315e-02 2.350 0.0188 *
MedHx_CVDyes 3.844e-01 1.469e+00 2.975e-01 1.292 0.1963
stenose0-49% -1.896e+01 5.807e-09 1.484e+04 -0.001 0.9990
stenose50-70% -5.293e-01 5.890e-01 1.246e+00 -0.425 0.6709
stenose70-90% -6.292e-01 5.330e-01 1.124e+00 -0.560 0.5757
stenose90-99% -6.563e-01 5.188e-01 1.137e+00 -0.577 0.5638
stenose100% (Occlusion) 2.429e-01 1.275e+00 1.526e+00 0.159 0.8736
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.916e+01 4.783e-09 1.921e+04 -0.001 0.9992
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.268e+00 7.887e-01 0.71531 2.247
Age 1.041e+00 9.602e-01 1.00595 1.078
Gendermale 9.890e-01 1.011e+00 0.54656 1.789
ORdate_year2003 7.400e-01 1.351e+00 0.19590 2.795
ORdate_year2004 7.641e-01 1.309e+00 0.21898 2.666
ORdate_year2005 1.377e+00 7.262e-01 0.42071 4.508
ORdate_year2006 1.156e+00 8.649e-01 0.34088 3.922
ORdate_year2007 5.940e-01 1.683e+00 0.14399 2.451
ORdate_year2008 7.666e-01 1.304e+00 0.18607 3.159
ORdate_year2009 9.724e-09 1.028e+08 0.00000 Inf
ORdate_year2010 8.547e-01 1.170e+00 0.23216 3.147
ORdate_year2011 5.191e-01 1.927e+00 0.12428 2.168
ORdate_year2012 1.304e+00 7.668e-01 0.35051 4.852
ORdate_year2013 7.156e-09 1.397e+08 0.00000 Inf
ORdate_year2014 NA NA NA NA
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Hypertension.compositeno 9.779e-01 1.023e+00 0.42582 2.246
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.759e-01 1.025e+00 0.52019 1.831
SmokerStatusEx-smoker 9.284e-01 1.077e+00 0.52083 1.655
SmokerStatusNever smoked 3.983e-01 2.511e+00 0.14289 1.110
Med.Statin.LLDno 1.403e+00 7.130e-01 0.77628 2.534
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.527e+00 6.549e-01 0.72761 3.204
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.958e-01 1.004e+00 0.98207 1.010
BMI 1.081e+00 9.251e-01 1.01301 1.154
MedHx_CVDyes 1.469e+00 6.809e-01 0.81979 2.631
stenose0-49% 5.807e-09 1.722e+08 0.00000 Inf
stenose50-70% 5.890e-01 1.698e+00 0.05125 6.768
stenose70-90% 5.330e-01 1.876e+00 0.05886 4.827
stenose90-99% 5.188e-01 1.928e+00 0.05588 4.816
stenose100% (Occlusion) 1.275e+00 7.844e-01 0.06401 25.394
stenoseNA NA NA NA NA
stenose50-99% 4.783e-09 2.091e+08 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.727 (se = 0.03 )
Likelihood ratio test= 41.46 on 29 df, p=0.06
Wald test = 19.48 on 29 df, p=0.9
Score (logrank) test = 36.13 on 29 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: 0.237314
Standard error............: 0.292024
Odds ratio (effect size)..: 1.268
Lower 95% CI..............: 0.715
Upper 95% CI..............: 2.247
T-value...................: 0.812652
P-value...................: 0.4164178
Sample size in model......: 1029
Number of events..........: 59
> processing [MCP1_rank]; 3 out of 3 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 493, number of events= 29
(1930 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -5.186e-01 5.953e-01 3.999e-01 -1.297 0.195
Age 1.587e-02 1.016e+00 2.485e-02 0.639 0.523
Gendermale 7.131e-02 1.074e+00 4.437e-01 0.161 0.872
ORdate_year2003 -6.306e-01 5.323e-01 6.431e-01 -0.980 0.327
ORdate_year2004 -4.388e-01 6.448e-01 5.877e-01 -0.747 0.455
ORdate_year2005 -4.641e-01 6.287e-01 5.911e-01 -0.785 0.432
ORdate_year2006 1.454e-01 1.156e+00 9.006e-01 0.161 0.872
ORdate_year2007 NA NA 0.000e+00 NA NA
ORdate_year2008 NA NA 0.000e+00 NA NA
ORdate_year2009 NA NA 0.000e+00 NA NA
ORdate_year2010 NA NA 0.000e+00 NA NA
ORdate_year2011 NA NA 0.000e+00 NA NA
ORdate_year2012 NA NA 0.000e+00 NA NA
ORdate_year2013 NA NA 0.000e+00 NA NA
ORdate_year2014 NA NA 0.000e+00 NA NA
ORdate_year2015 NA NA 0.000e+00 NA NA
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
Hypertension.compositeno -8.230e-01 4.391e-01 7.576e-01 -1.086 0.277
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 2.449e-01 1.277e+00 4.598e-01 0.533 0.594
SmokerStatusEx-smoker -5.660e-01 5.678e-01 4.210e-01 -1.345 0.179
SmokerStatusNever smoked -3.250e-01 7.225e-01 6.191e-01 -0.525 0.600
Med.Statin.LLDno -4.666e-02 9.544e-01 4.586e-01 -0.102 0.919
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno -1.411e-01 8.684e-01 6.765e-01 -0.209 0.835
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD 2.998e-03 1.003e+00 1.028e-02 0.292 0.771
BMI -7.989e-03 9.920e-01 4.989e-02 -0.160 0.873
MedHx_CVDyes 2.593e-01 1.296e+00 4.188e-01 0.619 0.536
stenose0-49% -1.870e+01 7.594e-09 1.325e+04 -0.001 0.999
stenose50-70% -1.863e+01 8.099e-09 4.984e+03 -0.004 0.997
stenose70-90% -1.346e+00 2.602e-01 1.149e+00 -1.172 0.241
stenose90-99% -1.585e+00 2.049e-01 1.164e+00 -1.362 0.173
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 5.953e-01 1.680e+00 0.27190 1.303
Age 1.016e+00 9.843e-01 0.96770 1.067
Gendermale 1.074e+00 9.312e-01 0.45005 2.563
ORdate_year2003 5.323e-01 1.879e+00 0.15091 1.877
ORdate_year2004 6.448e-01 1.551e+00 0.20380 2.040
ORdate_year2005 6.287e-01 1.591e+00 0.19737 2.003
ORdate_year2006 1.156e+00 8.647e-01 0.19793 6.757
ORdate_year2007 NA NA NA NA
ORdate_year2008 NA NA NA NA
ORdate_year2009 NA NA NA NA
ORdate_year2010 NA NA NA NA
ORdate_year2011 NA NA NA NA
ORdate_year2012 NA NA NA NA
ORdate_year2013 NA NA NA NA
ORdate_year2014 NA NA NA NA
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Hypertension.compositeno 4.391e-01 2.277e+00 0.09947 1.938
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.277e+00 7.828e-01 0.51879 3.146
SmokerStatusEx-smoker 5.678e-01 1.761e+00 0.24880 1.296
SmokerStatusNever smoked 7.225e-01 1.384e+00 0.21474 2.431
Med.Statin.LLDno 9.544e-01 1.048e+00 0.38848 2.345
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 8.684e-01 1.151e+00 0.23060 3.270
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 1.003e+00 9.970e-01 0.98299 1.023
BMI 9.920e-01 1.008e+00 0.89963 1.094
MedHx_CVDyes 1.296e+00 7.716e-01 0.57036 2.945
stenose0-49% 7.594e-09 1.317e+08 0.00000 Inf
stenose50-70% 8.099e-09 1.235e+08 0.00000 Inf
stenose70-90% 2.602e-01 3.844e+00 0.02739 2.471
stenose90-99% 2.049e-01 4.881e+00 0.02093 2.005
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.668 (se = 0.047 )
Likelihood ratio test= 11.3 on 20 df, p=0.9
Wald test = 8.6 on 20 df, p=1
Score (logrank) test = 10.53 on 20 df, p=1
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_rank
Effect size...............: -0.518645
Standard error............: 0.399851
Odds ratio (effect size)..: 0.595
Lower 95% CI..............: 0.272
Upper 95% CI..............: 1.304
T-value...................: -1.297096
P-value...................: 0.1945982
Sample size in model......: 493
Number of events..........: 29
* Analyzing the effect of plaque proteins on [epcoronary.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 3 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34102,0.00105) [ 0.00105,3.34102]
599 599
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1029, number of events= 78
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] -1.096e-01 8.962e-01 2.441e-01 -0.449 0.65325
Age 2.992e-03 1.003e+00 1.579e-02 0.190 0.84968
Gendermale 8.257e-01 2.284e+00 3.021e-01 2.733 0.00627 **
ORdate_year2003 1.891e-01 1.208e+00 4.745e-01 0.399 0.69024
ORdate_year2004 -7.166e-01 4.884e-01 5.244e-01 -1.367 0.17175
ORdate_year2005 -4.229e-01 6.551e-01 5.265e-01 -0.803 0.42180
ORdate_year2006 -4.844e-01 6.161e-01 5.277e-01 -0.918 0.35870
ORdate_year2007 -7.387e-01 4.777e-01 5.987e-01 -1.234 0.21725
ORdate_year2008 -1.913e-01 8.259e-01 5.683e-01 -0.337 0.73642
ORdate_year2009 -5.460e-01 5.793e-01 5.976e-01 -0.914 0.36092
ORdate_year2010 -1.136e+00 3.211e-01 7.041e-01 -1.613 0.10668
ORdate_year2011 -1.181e+00 3.071e-01 7.019e-01 -1.682 0.09256 .
ORdate_year2012 3.458e-01 1.413e+00 5.234e-01 0.661 0.50882
ORdate_year2013 9.383e-01 2.556e+00 9.568e-01 0.981 0.32674
ORdate_year2014 NA NA 0.000e+00 NA NA
ORdate_year2015 NA NA 0.000e+00 NA NA
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
Hypertension.compositeno -1.010e+00 3.641e-01 5.284e-01 -1.912 0.05585 .
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -1.266e-01 8.811e-01 2.814e-01 -0.450 0.65277
SmokerStatusEx-smoker -5.708e-01 5.651e-01 2.607e-01 -2.190 0.02856 *
SmokerStatusNever smoked -2.362e-01 7.896e-01 3.693e-01 -0.640 0.52234
Med.Statin.LLDno 6.546e-02 1.068e+00 2.790e-01 0.235 0.81452
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 4.346e-01 1.544e+00 3.400e-01 1.278 0.20122
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.025e-02 9.800e-01 6.156e-03 -3.289 0.00101 **
BMI 1.482e-02 1.015e+00 3.334e-02 0.444 0.65676
MedHx_CVDyes 6.845e-01 1.983e+00 2.819e-01 2.429 0.01516 *
stenose0-49% -1.449e+01 5.081e-07 3.179e+03 -0.005 0.99636
stenose50-70% -5.016e-01 6.056e-01 1.535e+00 -0.327 0.74378
stenose70-90% 1.047e+00 2.849e+00 1.218e+00 0.859 0.39019
stenose90-99% 9.371e-01 2.553e+00 1.215e+00 0.771 0.44069
stenose100% (Occlusion) -1.421e+01 6.748e-07 2.515e+03 -0.006 0.99549
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% 1.271e+00 3.565e+00 1.600e+00 0.794 0.42704
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 8.962e-01 1.116e+00 0.55544 1.4459
Age 1.003e+00 9.970e-01 0.97244 1.0345
Gendermale 2.284e+00 4.379e-01 1.26315 4.1283
ORdate_year2003 1.208e+00 8.277e-01 0.47672 3.0618
ORdate_year2004 4.884e-01 2.047e+00 0.17476 1.3650
ORdate_year2005 6.551e-01 1.526e+00 0.23346 1.8385
ORdate_year2006 6.161e-01 1.623e+00 0.21898 1.7332
ORdate_year2007 4.777e-01 2.093e+00 0.14776 1.5445
ORdate_year2008 8.259e-01 1.211e+00 0.27115 2.5156
ORdate_year2009 5.793e-01 1.726e+00 0.17955 1.8688
ORdate_year2010 3.211e-01 3.114e+00 0.08079 1.2764
ORdate_year2011 3.071e-01 3.256e+00 0.07759 1.2154
ORdate_year2012 1.413e+00 7.076e-01 0.50657 3.9421
ORdate_year2013 2.556e+00 3.913e-01 0.39183 16.6680
ORdate_year2014 NA NA NA NA
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Hypertension.compositeno 3.641e-01 2.747e+00 0.12925 1.0256
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 8.811e-01 1.135e+00 0.50753 1.5295
SmokerStatusEx-smoker 5.651e-01 1.770e+00 0.33899 0.9419
SmokerStatusNever smoked 7.896e-01 1.266e+00 0.38290 1.6283
Med.Statin.LLDno 1.068e+00 9.366e-01 0.61790 1.8448
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.544e+00 6.476e-01 0.79307 3.0070
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.800e-01 1.020e+00 0.96820 0.9919
BMI 1.015e+00 9.853e-01 0.95072 1.0835
MedHx_CVDyes 1.983e+00 5.043e-01 1.14121 3.4452
stenose0-49% 5.081e-07 1.968e+06 0.00000 Inf
stenose50-70% 6.056e-01 1.651e+00 0.02992 12.2566
stenose70-90% 2.849e+00 3.510e-01 0.26153 31.0383
stenose90-99% 2.553e+00 3.918e-01 0.23573 27.6415
stenose100% (Occlusion) 6.748e-07 1.482e+06 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 3.565e+00 2.805e-01 0.15479 82.1134
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.743 (se = 0.027 )
Likelihood ratio test= 66.14 on 29 df, p=1e-04
Wald test = 58.95 on 29 df, p=8e-04
Score (logrank) test = 63.72 on 29 df, p=2e-04
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: -0.109644
Standard error............: 0.244058
Odds ratio (effect size)..: 0.896
Lower 95% CI..............: 0.555
Upper 95% CI..............: 1.446
T-value...................: -0.449252
P-value...................: 0.6532497
Sample size in model......: 1029
Number of events..........: 78
> processing [MCP1_pg_ml_2015_rank]; 2 out of 3 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1029, number of events= 78
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 2.505e-01 1.285e+00 2.498e-01 1.003 0.31595
Age 3.056e-03 1.003e+00 1.584e-02 0.193 0.84702
Gendermale 7.891e-01 2.201e+00 3.034e-01 2.601 0.00929 **
ORdate_year2003 1.939e-01 1.214e+00 4.750e-01 0.408 0.68306
ORdate_year2004 -6.007e-01 5.485e-01 5.308e-01 -1.132 0.25778
ORdate_year2005 -3.293e-01 7.195e-01 5.299e-01 -0.621 0.53438
ORdate_year2006 -4.507e-01 6.372e-01 5.266e-01 -0.856 0.39206
ORdate_year2007 -7.470e-01 4.738e-01 5.981e-01 -1.249 0.21164
ORdate_year2008 -1.942e-01 8.235e-01 5.684e-01 -0.342 0.73267
ORdate_year2009 -6.198e-01 5.381e-01 5.989e-01 -1.035 0.30070
ORdate_year2010 -1.215e+00 2.966e-01 7.006e-01 -1.735 0.08277 .
ORdate_year2011 -1.263e+00 2.828e-01 7.006e-01 -1.803 0.07146 .
ORdate_year2012 2.945e-01 1.342e+00 5.236e-01 0.562 0.57385
ORdate_year2013 8.966e-01 2.451e+00 9.631e-01 0.931 0.35190
ORdate_year2014 NA NA 0.000e+00 NA NA
ORdate_year2015 NA NA 0.000e+00 NA NA
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
Hypertension.compositeno -1.056e+00 3.480e-01 5.286e-01 -1.997 0.04583 *
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -9.698e-02 9.076e-01 2.801e-01 -0.346 0.72918
SmokerStatusEx-smoker -5.792e-01 5.604e-01 2.610e-01 -2.219 0.02646 *
SmokerStatusNever smoked -2.749e-01 7.596e-01 3.704e-01 -0.742 0.45795
Med.Statin.LLDno 4.080e-02 1.042e+00 2.797e-01 0.146 0.88404
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 4.072e-01 1.503e+00 3.399e-01 1.198 0.23095
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.010e-02 9.801e-01 6.121e-03 -3.283 0.00103 **
BMI 1.647e-02 1.017e+00 3.377e-02 0.488 0.62568
MedHx_CVDyes 6.978e-01 2.009e+00 2.821e-01 2.473 0.01339 *
stenose0-49% -1.449e+01 5.114e-07 3.142e+03 -0.005 0.99632
stenose50-70% -4.387e-01 6.449e-01 1.537e+00 -0.285 0.77532
stenose70-90% 1.133e+00 3.105e+00 1.229e+00 0.922 0.35676
stenose90-99% 1.025e+00 2.786e+00 1.227e+00 0.835 0.40353
stenose100% (Occlusion) -1.406e+01 7.852e-07 2.535e+03 -0.006 0.99557
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% 1.291e+00 3.637e+00 1.611e+00 0.802 0.42283
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.285e+00 7.784e-01 0.78737 2.0959
Age 1.003e+00 9.969e-01 0.97240 1.0347
Gendermale 2.201e+00 4.543e-01 1.21470 3.9896
ORdate_year2003 1.214e+00 8.237e-01 0.47855 3.0797
ORdate_year2004 5.485e-01 1.823e+00 0.19379 1.5522
ORdate_year2005 7.195e-01 1.390e+00 0.25465 2.0327
ORdate_year2006 6.372e-01 1.569e+00 0.22700 1.7885
ORdate_year2007 4.738e-01 2.111e+00 0.14673 1.5298
ORdate_year2008 8.235e-01 1.214e+00 0.27031 2.5090
ORdate_year2009 5.381e-01 1.859e+00 0.16637 1.7401
ORdate_year2010 2.966e-01 3.372e+00 0.07514 1.1708
ORdate_year2011 2.828e-01 3.535e+00 0.07165 1.1166
ORdate_year2012 1.342e+00 7.449e-01 0.48105 3.7461
ORdate_year2013 2.451e+00 4.080e-01 0.37117 16.1874
ORdate_year2014 NA NA NA NA
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Hypertension.compositeno 3.480e-01 2.874e+00 0.12348 0.9806
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.076e-01 1.102e+00 0.52412 1.5715
SmokerStatusEx-smoker 5.604e-01 1.785e+00 0.33600 0.9345
SmokerStatusNever smoked 7.596e-01 1.316e+00 0.36752 1.5700
Med.Statin.LLDno 1.042e+00 9.600e-01 0.60202 1.8023
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.503e+00 6.655e-01 0.77178 2.9256
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.801e-01 1.020e+00 0.96842 0.9919
BMI 1.017e+00 9.837e-01 0.95150 1.0862
MedHx_CVDyes 2.009e+00 4.977e-01 1.15585 3.4930
stenose0-49% 5.114e-07 1.955e+06 0.00000 Inf
stenose50-70% 6.449e-01 1.551e+00 0.03170 13.1188
stenose70-90% 3.105e+00 3.221e-01 0.27898 34.5565
stenose90-99% 2.786e+00 3.589e-01 0.25169 30.8442
stenose100% (Occlusion) 7.852e-07 1.274e+06 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 3.637e+00 2.749e-01 0.15471 85.5055
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.746 (se = 0.028 )
Likelihood ratio test= 66.95 on 29 df, p=8e-05
Wald test = 59.79 on 29 df, p=7e-04
Score (logrank) test = 64.44 on 29 df, p=2e-04
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: 0.250465
Standard error............: 0.249761
Odds ratio (effect size)..: 1.285
Lower 95% CI..............: 0.787
Upper 95% CI..............: 2.096
T-value...................: 1.002821
P-value...................: 0.3159475
Sample size in model......: 1029
Number of events..........: 78
> processing [MCP1_rank]; 3 out of 3 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 493, number of events= 42
(1930 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -6.664e-03 9.934e-01 3.313e-01 -0.020 0.9840
Age 4.076e-02 1.042e+00 2.305e-02 1.769 0.0770 .
Gendermale 9.719e-01 2.643e+00 4.709e-01 2.064 0.0390 *
ORdate_year2003 -3.203e-01 7.259e-01 4.535e-01 -0.706 0.4800
ORdate_year2004 -9.864e-01 3.729e-01 4.946e-01 -1.994 0.0461 *
ORdate_year2005 -7.907e-01 4.535e-01 4.801e-01 -1.647 0.0996 .
ORdate_year2006 -9.310e-01 3.941e-01 8.371e-01 -1.112 0.2660
ORdate_year2007 NA NA 0.000e+00 NA NA
ORdate_year2008 NA NA 0.000e+00 NA NA
ORdate_year2009 NA NA 0.000e+00 NA NA
ORdate_year2010 NA NA 0.000e+00 NA NA
ORdate_year2011 NA NA 0.000e+00 NA NA
ORdate_year2012 NA NA 0.000e+00 NA NA
ORdate_year2013 NA NA 0.000e+00 NA NA
ORdate_year2014 NA NA 0.000e+00 NA NA
ORdate_year2015 NA NA 0.000e+00 NA NA
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
Hypertension.compositeno -2.161e-01 8.056e-01 5.468e-01 -0.395 0.6927
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 5.210e-01 1.684e+00 3.595e-01 1.449 0.1473
SmokerStatusEx-smoker -3.852e-01 6.803e-01 3.504e-01 -1.099 0.2716
SmokerStatusNever smoked 2.323e-03 1.002e+00 5.080e-01 0.005 0.9964
Med.Statin.LLDno -4.831e-02 9.528e-01 3.661e-01 -0.132 0.8950
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 5.872e-01 1.799e+00 4.652e-01 1.262 0.2069
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.535e-02 9.848e-01 8.534e-03 -1.799 0.0721 .
BMI 4.406e-02 1.045e+00 4.256e-02 1.035 0.3006
MedHx_CVDyes 1.597e-01 1.173e+00 3.495e-01 0.457 0.6477
stenose0-49% -4.389e-01 6.448e-01 9.164e+03 0.000 1.0000
stenose50-70% 1.610e+01 9.796e+06 5.270e+03 0.003 0.9976
stenose70-90% 1.620e+01 1.086e+07 5.270e+03 0.003 0.9975
stenose90-99% 1.607e+01 9.538e+06 5.270e+03 0.003 0.9976
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 9.934e-01 1.007e+00 0.5189 1.9016
Age 1.042e+00 9.601e-01 0.9956 1.0897
Gendermale 2.643e+00 3.784e-01 1.0503 6.6514
ORdate_year2003 7.259e-01 1.378e+00 0.2984 1.7656
ORdate_year2004 3.729e-01 2.681e+00 0.1415 0.9831
ORdate_year2005 4.535e-01 2.205e+00 0.1770 1.1622
ORdate_year2006 3.941e-01 2.537e+00 0.0764 2.0333
ORdate_year2007 NA NA NA NA
ORdate_year2008 NA NA NA NA
ORdate_year2009 NA NA NA NA
ORdate_year2010 NA NA NA NA
ORdate_year2011 NA NA NA NA
ORdate_year2012 NA NA NA NA
ORdate_year2013 NA NA NA NA
ORdate_year2014 NA NA NA NA
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Hypertension.compositeno 8.056e-01 1.241e+00 0.2759 2.3527
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.684e+00 5.939e-01 0.8322 3.4066
SmokerStatusEx-smoker 6.803e-01 1.470e+00 0.3423 1.3520
SmokerStatusNever smoked 1.002e+00 9.977e-01 0.3703 2.7128
Med.Statin.LLDno 9.528e-01 1.049e+00 0.4649 1.9528
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.799e+00 5.559e-01 0.7228 4.4770
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.848e-01 1.015e+00 0.9684 1.0014
BMI 1.045e+00 9.569e-01 0.9614 1.1360
MedHx_CVDyes 1.173e+00 8.524e-01 0.5914 2.3274
stenose0-49% 6.448e-01 1.551e+00 0.0000 Inf
stenose50-70% 9.796e+06 1.021e-07 0.0000 Inf
stenose70-90% 1.086e+07 9.204e-08 0.0000 Inf
stenose90-99% 9.538e+06 1.048e-07 0.0000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.731 (se = 0.036 )
Likelihood ratio test= 26.55 on 20 df, p=0.1
Wald test = 17.64 on 20 df, p=0.6
Score (logrank) test = 26.48 on 20 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_rank
Effect size...............: -0.006664
Standard error............: 0.3313
Odds ratio (effect size)..: 0.993
Lower 95% CI..............: 0.519
Upper 95% CI..............: 1.902
T-value...................: -0.020116
P-value...................: 0.9839507
Sample size in model......: 493
Number of events..........: 42
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 3 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34102,0.00105) [ 0.00105,3.34102]
599 599
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1029, number of events= 33
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] -3.140e-02 9.691e-01 3.909e-01 -0.080 0.935986
Age 6.855e-02 1.071e+00 2.734e-02 2.507 0.012184 *
Gendermale 1.257e+00 3.514e+00 5.710e-01 2.201 0.027752 *
ORdate_year2003 -3.493e-01 7.052e-01 7.923e-01 -0.441 0.659346
ORdate_year2004 -4.130e-01 6.617e-01 7.540e-01 -0.548 0.583910
ORdate_year2005 -2.726e-01 7.614e-01 7.842e-01 -0.348 0.728105
ORdate_year2006 -4.256e-01 6.534e-01 7.915e-01 -0.538 0.590757
ORdate_year2007 -6.295e-01 5.328e-01 8.677e-01 -0.726 0.468137
ORdate_year2008 -1.500e+00 2.232e-01 1.184e+00 -1.267 0.205119
ORdate_year2009 -1.720e+00 1.790e-01 1.208e+00 -1.424 0.154429
ORdate_year2010 -1.016e+00 3.619e-01 9.548e-01 -1.064 0.287140
ORdate_year2011 -1.756e+00 1.727e-01 1.185e+00 -1.482 0.138314
ORdate_year2012 3.301e-01 1.391e+00 8.299e-01 0.398 0.690793
ORdate_year2013 2.612e-01 1.298e+00 1.546e+00 0.169 0.865840
ORdate_year2014 NA NA 0.000e+00 NA NA
ORdate_year2015 NA NA 0.000e+00 NA NA
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
Hypertension.compositeno -1.779e+01 1.873e-08 4.051e+03 -0.004 0.996495
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -1.483e-01 8.622e-01 4.463e-01 -0.332 0.739699
SmokerStatusEx-smoker -4.660e-01 6.275e-01 4.213e-01 -1.106 0.268657
SmokerStatusNever smoked -3.840e-01 6.811e-01 6.350e-01 -0.605 0.545349
Med.Statin.LLDno 3.489e-02 1.036e+00 4.442e-01 0.079 0.937402
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.179e+00 3.250e+00 4.298e-01 2.742 0.006105 **
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -3.570e-02 9.649e-01 9.885e-03 -3.611 0.000305 ***
BMI 9.230e-02 1.097e+00 5.371e-02 1.719 0.085699 .
MedHx_CVDyes 7.441e-01 2.105e+00 4.748e-01 1.567 0.117016
stenose0-49% -1.925e+01 4.365e-09 2.730e+04 -0.001 0.999437
stenose50-70% 2.928e-01 1.340e+00 1.484e+00 0.197 0.843637
stenose70-90% -5.827e-01 5.584e-01 1.424e+00 -0.409 0.682417
stenose90-99% -2.460e-01 7.820e-01 1.430e+00 -0.172 0.863457
stenose100% (Occlusion) -1.845e+01 9.752e-09 2.012e+04 -0.001 0.999268
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.947e+01 3.511e-09 3.503e+04 -0.001 0.999557
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 9.691e-01 1.032e+00 0.45042 2.0850
Age 1.071e+00 9.337e-01 1.01506 1.1299
Gendermale 3.514e+00 2.846e-01 1.14741 10.7606
ORdate_year2003 7.052e-01 1.418e+00 0.14924 3.3323
ORdate_year2004 6.617e-01 1.511e+00 0.15096 2.9004
ORdate_year2005 7.614e-01 1.313e+00 0.16372 3.5408
ORdate_year2006 6.534e-01 1.531e+00 0.13851 3.0822
ORdate_year2007 5.328e-01 1.877e+00 0.09728 2.9187
ORdate_year2008 2.232e-01 4.480e+00 0.02194 2.2706
ORdate_year2009 1.790e-01 5.587e+00 0.01677 1.9106
ORdate_year2010 3.619e-01 2.763e+00 0.05570 2.3516
ORdate_year2011 1.727e-01 5.789e+00 0.01694 1.7615
ORdate_year2012 1.391e+00 7.188e-01 0.27351 7.0755
ORdate_year2013 1.298e+00 7.701e-01 0.06273 26.8779
ORdate_year2014 NA NA NA NA
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Hypertension.compositeno 1.873e-08 5.340e+07 0.00000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 8.622e-01 1.160e+00 0.35947 2.0679
SmokerStatusEx-smoker 6.275e-01 1.594e+00 0.27482 1.4329
SmokerStatusNever smoked 6.811e-01 1.468e+00 0.19622 2.3644
Med.Statin.LLDno 1.036e+00 9.657e-01 0.43353 2.4733
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 3.250e+00 3.077e-01 1.39955 7.5456
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.649e-01 1.036e+00 0.94642 0.9838
BMI 1.097e+00 9.118e-01 0.98712 1.2184
MedHx_CVDyes 2.105e+00 4.751e-01 0.82997 5.3369
stenose0-49% 4.365e-09 2.291e+08 0.00000 Inf
stenose50-70% 1.340e+00 7.462e-01 0.07307 24.5784
stenose70-90% 5.584e-01 1.791e+00 0.03425 9.1027
stenose90-99% 7.820e-01 1.279e+00 0.04740 12.8994
stenose100% (Occlusion) 9.752e-09 1.025e+08 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 3.511e-09 2.848e+08 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.858 (se = 0.027 )
Likelihood ratio test= 68.91 on 29 df, p=4e-05
Wald test = 27.5 on 29 df, p=0.5
Score (logrank) test = 62.71 on 29 df, p=3e-04
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: -0.031397
Standard error............: 0.390912
Odds ratio (effect size)..: 0.969
Lower 95% CI..............: 0.45
Upper 95% CI..............: 2.085
T-value...................: -0.080316
P-value...................: 0.9359857
Sample size in model......: 1029
Number of events..........: 33
> processing [MCP1_pg_ml_2015_rank]; 2 out of 3 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1029, number of events= 33
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.426e-01 1.153e+00 4.005e-01 0.356 0.721816
Age 6.840e-02 1.071e+00 2.740e-02 2.497 0.012541 *
Gendermale 1.254e+00 3.504e+00 5.719e-01 2.192 0.028345 *
ORdate_year2003 -3.537e-01 7.021e-01 7.910e-01 -0.447 0.654735
ORdate_year2004 -3.586e-01 6.987e-01 7.556e-01 -0.475 0.635092
ORdate_year2005 -2.165e-01 8.054e-01 7.870e-01 -0.275 0.783279
ORdate_year2006 -4.195e-01 6.574e-01 7.880e-01 -0.532 0.594444
ORdate_year2007 -6.501e-01 5.220e-01 8.694e-01 -0.748 0.454588
ORdate_year2008 -1.517e+00 2.193e-01 1.184e+00 -1.281 0.200226
ORdate_year2009 -1.774e+00 1.696e-01 1.212e+00 -1.464 0.143275
ORdate_year2010 -1.063e+00 3.454e-01 9.514e-01 -1.117 0.263851
ORdate_year2011 -1.799e+00 1.654e-01 1.188e+00 -1.515 0.129766
ORdate_year2012 2.980e-01 1.347e+00 8.314e-01 0.358 0.720019
ORdate_year2013 1.959e-01 1.216e+00 1.550e+00 0.126 0.899404
ORdate_year2014 NA NA 0.000e+00 NA NA
ORdate_year2015 NA NA 0.000e+00 NA NA
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
Hypertension.compositeno -1.781e+01 1.838e-08 4.036e+03 -0.004 0.996478
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -1.439e-01 8.659e-01 4.452e-01 -0.323 0.746430
SmokerStatusEx-smoker -4.723e-01 6.236e-01 4.215e-01 -1.121 0.262492
SmokerStatusNever smoked -3.913e-01 6.762e-01 6.356e-01 -0.616 0.538153
Med.Statin.LLDno 2.338e-02 1.024e+00 4.444e-01 0.053 0.958046
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.178e+00 3.248e+00 4.304e-01 2.737 0.006197 **
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -3.554e-02 9.651e-01 9.875e-03 -3.599 0.000319 ***
BMI 9.517e-02 1.100e+00 5.417e-02 1.757 0.078966 .
MedHx_CVDyes 7.522e-01 2.122e+00 4.756e-01 1.582 0.113754
stenose0-49% -1.931e+01 4.129e-09 2.728e+04 -0.001 0.999435
stenose50-70% 3.067e-01 1.359e+00 1.479e+00 0.207 0.835688
stenose70-90% -5.775e-01 5.613e-01 1.422e+00 -0.406 0.684666
stenose90-99% -2.341e-01 7.913e-01 1.428e+00 -0.164 0.869789
stenose100% (Occlusion) -1.841e+01 1.010e-08 2.013e+04 -0.001 0.999270
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.949e+01 3.420e-09 3.490e+04 -0.001 0.999554
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.153e+00 8.671e-01 0.52603 2.5284
Age 1.071e+00 9.339e-01 1.01481 1.1299
Gendermale 3.504e+00 2.854e-01 1.14222 10.7486
ORdate_year2003 7.021e-01 1.424e+00 0.14896 3.3089
ORdate_year2004 6.987e-01 1.431e+00 0.15890 3.0720
ORdate_year2005 8.054e-01 1.242e+00 0.17221 3.7663
ORdate_year2006 6.574e-01 1.521e+00 0.14031 3.0798
ORdate_year2007 5.220e-01 1.916e+00 0.09497 2.8687
ORdate_year2008 2.193e-01 4.559e+00 0.02152 2.2351
ORdate_year2009 1.696e-01 5.897e+00 0.01576 1.8250
ORdate_year2010 3.454e-01 2.895e+00 0.05353 2.2292
ORdate_year2011 1.654e-01 6.045e+00 0.01613 1.6962
ORdate_year2012 1.347e+00 7.423e-01 0.26409 6.8718
ORdate_year2013 1.216e+00 8.221e-01 0.05834 25.3616
ORdate_year2014 NA NA NA NA
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Hypertension.compositeno 1.838e-08 5.440e+07 0.00000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 8.659e-01 1.155e+00 0.36186 2.0721
SmokerStatusEx-smoker 6.236e-01 1.604e+00 0.27299 1.4245
SmokerStatusNever smoked 6.762e-01 1.479e+00 0.19458 2.3500
Med.Statin.LLDno 1.024e+00 9.769e-01 0.42841 2.4460
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 3.248e+00 3.079e-01 1.39726 7.5511
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.651e-01 1.036e+00 0.94658 0.9839
BMI 1.100e+00 9.092e-01 0.98905 1.2230
MedHx_CVDyes 2.122e+00 4.713e-01 0.83528 5.3894
stenose0-49% 4.129e-09 2.422e+08 0.00000 Inf
stenose50-70% 1.359e+00 7.359e-01 0.07489 24.6596
stenose70-90% 5.613e-01 1.782e+00 0.03457 9.1136
stenose90-99% 7.913e-01 1.264e+00 0.04816 12.9998
stenose100% (Occlusion) 1.010e-08 9.902e+07 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 3.420e-09 2.924e+08 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.859 (se = 0.027 )
Likelihood ratio test= 69.03 on 29 df, p=4e-05
Wald test = 27.11 on 29 df, p=0.6
Score (logrank) test = 62.76 on 29 df, p=3e-04
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: 0.142597
Standard error............: 0.400517
Odds ratio (effect size)..: 1.153
Lower 95% CI..............: 0.526
Upper 95% CI..............: 2.528
T-value...................: 0.356033
P-value...................: 0.7218158
Sample size in model......: 1029
Number of events..........: 33
> processing [MCP1_rank]; 3 out of 3 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 493, number of events= 23
(1930 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -2.622e-01 7.693e-01 4.494e-01 -0.584 0.5595
Age 5.013e-02 1.051e+00 3.195e-02 1.569 0.1167
Gendermale 1.054e+00 2.868e+00 6.793e-01 1.551 0.1209
ORdate_year2003 -1.271e+00 2.805e-01 7.323e-01 -1.736 0.0826 .
ORdate_year2004 -6.318e-01 5.316e-01 5.815e-01 -1.086 0.2773
ORdate_year2005 -9.060e-01 4.041e-01 6.295e-01 -1.439 0.1501
ORdate_year2006 -5.482e-02 9.467e-01 9.111e-01 -0.060 0.9520
ORdate_year2007 NA NA 0.000e+00 NA NA
ORdate_year2008 NA NA 0.000e+00 NA NA
ORdate_year2009 NA NA 0.000e+00 NA NA
ORdate_year2010 NA NA 0.000e+00 NA NA
ORdate_year2011 NA NA 0.000e+00 NA NA
ORdate_year2012 NA NA 0.000e+00 NA NA
ORdate_year2013 NA NA 0.000e+00 NA NA
ORdate_year2014 NA NA 0.000e+00 NA NA
ORdate_year2015 NA NA 0.000e+00 NA NA
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
Hypertension.compositeno -1.807e+01 1.421e-08 4.772e+03 -0.004 0.9970
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 4.995e-01 1.648e+00 5.415e-01 0.922 0.3563
SmokerStatusEx-smoker -6.120e-01 5.423e-01 4.861e-01 -1.259 0.2080
SmokerStatusNever smoked -1.704e-01 8.433e-01 7.487e-01 -0.228 0.8200
Med.Statin.LLDno 8.436e-01 2.325e+00 4.609e-01 1.830 0.0672 .
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.283e-01 1.389e+00 6.869e-01 0.478 0.6327
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.097e-02 9.793e-01 1.077e-02 -1.946 0.0516 .
BMI 5.405e-03 1.005e+00 6.169e-02 0.088 0.9302
MedHx_CVDyes 1.281e+00 3.599e+00 6.417e-01 1.996 0.0459 *
stenose0-49% -7.470e-01 4.738e-01 4.039e+04 0.000 1.0000
stenose50-70% 3.499e-01 1.419e+00 2.391e+04 0.000 1.0000
stenose70-90% 1.844e+01 1.021e+08 2.218e+04 0.001 0.9993
stenose90-99% 1.818e+01 7.897e+07 2.218e+04 0.001 0.9993
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 7.693e-01 1.300e+00 0.31887 1.856
Age 1.051e+00 9.511e-01 0.98758 1.119
Gendermale 2.868e+00 3.487e-01 0.75751 10.860
ORdate_year2003 2.805e-01 3.566e+00 0.06676 1.178
ORdate_year2004 5.316e-01 1.881e+00 0.17006 1.662
ORdate_year2005 4.041e-01 2.474e+00 0.11767 1.388
ORdate_year2006 9.467e-01 1.056e+00 0.15872 5.646
ORdate_year2007 NA NA NA NA
ORdate_year2008 NA NA NA NA
ORdate_year2009 NA NA NA NA
ORdate_year2010 NA NA NA NA
ORdate_year2011 NA NA NA NA
ORdate_year2012 NA NA NA NA
ORdate_year2013 NA NA NA NA
ORdate_year2014 NA NA NA NA
ORdate_year2015 NA NA NA NA
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
Hypertension.compositeno 1.421e-08 7.038e+07 0.00000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.648e+00 6.068e-01 0.57016 4.763
SmokerStatusEx-smoker 5.423e-01 1.844e+00 0.20916 1.406
SmokerStatusNever smoked 8.433e-01 1.186e+00 0.19438 3.659
Med.Statin.LLDno 2.325e+00 4.301e-01 0.94198 5.738
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.389e+00 7.201e-01 0.36130 5.337
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.793e-01 1.021e+00 0.95879 1.000
BMI 1.005e+00 9.946e-01 0.89092 1.135
MedHx_CVDyes 3.599e+00 2.778e-01 1.02330 12.658
stenose0-49% 4.738e-01 2.111e+00 0.00000 Inf
stenose50-70% 1.419e+00 7.047e-01 0.00000 Inf
stenose70-90% 1.021e+08 9.797e-09 0.00000 Inf
stenose90-99% 7.897e+07 1.266e-08 0.00000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.836 (se = 0.034 )
Likelihood ratio test= 36.94 on 20 df, p=0.01
Wald test = 16.64 on 20 df, p=0.7
Score (logrank) test = 33.1 on 20 df, p=0.03
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_rank
Effect size...............: -0.262229
Standard error............: 0.449375
Odds ratio (effect size)..: 0.769
Lower 95% CI..............: 0.319
Upper 95% CI..............: 1.856
T-value...................: -0.583541
P-value...................: 0.5595292
Sample size in model......: 493
Number of events..........: 23 
cat("- Edit the column names...\n")- Edit the column names...colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")- Correct the variable types...COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")- Writing results to Excel-file...head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")- Removing intermediate files...rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
rm(head.style)object 'head.style' not found30-days follow-up
MODEL 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
eptime = times30[i]
ep = endpoints30[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
ylim = c(0.75, 1),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 1 (Simple model)
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
# ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [days]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL1.30days.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.30days.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
#rm(head.style)
MODEL 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
eptime = times30[i]
ep = endpoints30[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
#as.integer(TEMP.DF[,ep] == "Excluded")
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
ylim = c(0.75, 1),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
# ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [days]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL2.30days.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.30days.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
rm(head.style)
90-days follow-up
MODEL 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
eptime = times90[i]
ep = endpoints90[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
ylim = c(0.75, 1),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 1 (Simple model)
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
# ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [days]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL1.90days.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.90days.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
#rm(head.style)
MODEL 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
eptime = times90[i]
ep = endpoints90[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
#as.integer(TEMP.DF[,ep] == "Excluded")
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
ylim = c(0.75, 1),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
# ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [days]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL2.90days.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.90days.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
rm(head.style)
Correlations
All biomarkers
We correlated plasma and plaque levels of the biomarkers.
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools))
install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")Skipping install of 'ggcorrplot' from a github remote, the SHA1 (c46b4cce) has not changed since last install.
Use `force = TRUE` to force installationlibrary(ggcorrplot)
# Creating matrix - inverse-rank transformation
# --------------------------------
# AEDB.CEA.temp <- subset(AEDB.CEA,
# select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
# TRAITS.BIN, TRAITS.CON.RANK)
# )
# AEDB.CEA.temp <- subset(AEDB.CEA,
# select = c("MCP1_rank", "MCP1_pg_ug_2015_rank",
# TRAITS.BIN, TRAITS.CON.RANK)
# )
AEDB.CEA.temp <- subset(AEDB.CEA,
select = c("MCP1_pg_ug_2015_rank",
TRAITS.BIN, TRAITS.CON.RANK)
)
AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$MAC_binned <- as.numeric(AEDB.CEA.temp$MAC_binned)
AEDB.CEA.temp$SMC_binned <- as.numeric(AEDB.CEA.temp$SMC_binned)
str(AEDB.CEA.temp)'data.frame': 2423 obs. of 10 variables:
$ MCP1_pg_ug_2015_rank: num 0.938 2.159 1.21 1.996 1.407 ...
$ CalcificationPlaque : num 1 1 1 1 2 2 2 2 1 2 ...
$ CollagenPlaque : num 2 2 2 2 1 2 2 2 2 1 ...
$ Fat10Perc : num 2 2 2 2 2 2 2 1 2 2 ...
$ IPH : num 2 2 2 1 2 2 2 2 2 2 ...
$ MAC_binned : num 1 1 1 1 1 1 2 1 1 1 ...
$ SMC_binned : num 1 1 2 2 1 1 1 2 2 1 ...
$ Macrophages_rank : num 1.121 1.366 0.722 0.396 -1.013 ...
$ SMC_rank : num 1.13161 0.00148 1.42686 1.26957 0.34377 ...
$ VesselDensity_rank : num -0.978 -0.774 0.717 1.1 1.518 ...AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)
corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK,
use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
method = "spearman"), 3)
# corr_biomarkers.rank
corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")Cannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with ties# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank,
method = "square",
type = "lower",
title = "Cross biomarker correlations",
show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
show.diag = TRUE,
hc.order = FALSE,
lab = FALSE,
digits = 3,
# p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
colors = c("#1290D9", "#FFFFFF", "#E55738"))
# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
ut <- upper.tri(cormat)
data.frame(
biomarker_row = rownames(cormat)[row(cormat)[ut]],
biomarker_column = rownames(cormat)[col(cormat)[ut]],
spearman_cor =(cormat)[ut],
pval = pmat[ut]
)
}
corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)
# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall",
"spearman"), ...)
{
x = checkData(R, method = "matrix")
if (missing(method))
method = method[1]
cormeth <- method
panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs",
method = cormeth, cex.cor, ...) {
usr <- par("usr")
on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r <- cor(x, y, use = use, method = method)
txt <- format(c(r, 0.123456789), digits = digits)[1]
txt <- paste(prefix, txt, sep = "")
if (missing(cex.cor))
cex <- 0.8/strwidth(txt)
test <- cor.test(as.numeric(x), as.numeric(y), method = method)
Signif <- symnum(test$p.value, corr = FALSE, na = FALSE,
cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***",
"**", "*", ".", " "))
text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
text(0.8, 0.8, Signif, cex = cex, col = 2)
}
f <- function(t) {
dnorm(t, mean = mean(x), sd = sd.xts(x))
}
dotargs <- list(...)
dotargs$method <- NULL
rm(method)
hist.panel = function(x, ... = NULL) {
par(new = TRUE)
hist(x, col = "#1290D9", probability = TRUE, axes = FALSE,
# hist(x, col = "light gray", probability = TRUE, axes = FALSE,
main = "", breaks = "FD")
lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
rug(x)
}
if (histogram)
pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor,
diag.panel = hist.panel, ...)
else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}
chart.Correlation.new(AEDB.CEA.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)
# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")
# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables
library(GGally)
# From the help page:
# ggpairs(AEDB.CEA,
# columns = c("MCP1_rank", "MCP1_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK),
# columnLabels = c("MCP1 (plasma)", "MCP1",
# "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density"),
# method = c("spearman"),
# # ggplot2::aes(colour = Gender),
# progress = FALSE)
ggpairs(AEDB.CEA,
columns = c("MCP1_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK),
columnLabels = c("MCP1",
"Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages (binned)", "SMC (binned)", "Macrophages", "SMC", "Vessel density"),
method = c("spearman"),
# ggplot2::aes(colour = Gender),
progress = FALSE)Extra arguments: 'method' are being ignored. If these are meant to be aesthetics, submit them using the 'mapping' variable within ggpairs with ggplot2::aes or ggplot2::aes_string.
Circulating MCP1
Finally, we explored in a sub-sample, where circulating MCP-1 levels are available, the following:
- A correlation between MCP-1 levels in the plaque and circulating MCP-1 levels
- Associations of circulating MCP-1 levels with plaque vulnerability characteristics
- Associations of circulating MCP-1 levels with the status of the plaque in terms of presence of symptoms (symptomatic vs. asymptomatic)
- Associations of circulating MCP-1 levels with the primary composite endpoint of secondary cardiovascular events.
NOT AVAILABLE YET
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools))
install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")
library(ggcorrplot)
# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA,
select = c("MCP1_rank",
TRAITS.BIN, TRAITS.CON.RANK,
"Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
)
AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.plasma.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)
corr_biomarkers_plasma.rank <- round(cor(AEDB.CEA.matrix.plasma.RANK,
use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
method = "spearman"), 3)
# corr_biomarkers.rank
corr_biomarkers_plasma_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.plasma.RANK, use = "pairwise.complete.obs", method = "spearman")
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers_plasma.rank,
method = "square",
type = "lower",
title = "Cross biomarker correlations",
show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
show.diag = TRUE,
hc.order = FALSE,
lab = FALSE,
digits = 3,
# p.mat = corr_biomarkers_plasma_p.rank, sig.level = 0.05,
colors = c("#1290D9", "#FFFFFF", "#E55738"))
# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
ut <- upper.tri(cormat)
data.frame(
biomarker_row = rownames(cormat)[row(cormat)[ut]],
biomarker_column = rownames(cormat)[col(cormat)[ut]],
spearman_cor =(cormat)[ut],
pval = pmat[ut]
)
}
corr_biomarkers_plasma.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers_plasma.rank, corr_biomarkers_plasma_p.rank))
DT::datatable(corr_biomarkers_plasma.rank.df)
# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall",
"spearman"), ...)
{
x = checkData(R, method = "matrix")
if (missing(method))
method = method[1]
cormeth <- method
panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs",
method = cormeth, cex.cor, ...) {
usr <- par("usr")
on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r <- cor(x, y, use = use, method = method)
txt <- format(c(r, 0.123456789), digits = digits)[1]
txt <- paste(prefix, txt, sep = "")
if (missing(cex.cor))
cex <- 0.8/strwidth(txt)
test <- cor.test(as.numeric(x), as.numeric(y), method = method)
Signif <- symnum(test$p.value, corr = FALSE, na = FALSE,
cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***",
"**", "*", ".", " "))
text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
text(0.8, 0.8, Signif, cex = cex, col = 2)
}
f <- function(t) {
dnorm(t, mean = mean(x), sd = sd.xts(x))
}
dotargs <- list(...)
dotargs$method <- NULL
rm(method)
hist.panel = function(x, ... = NULL) {
par(new = TRUE)
hist(x, col = "#1290D9", probability = TRUE, axes = FALSE,
# hist(x, col = "light gray", probability = TRUE, axes = FALSE,
main = "", breaks = "FD")
lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
rug(x)
}
if (histogram)
pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor,
diag.panel = hist.panel, ...)
else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}
chart.Correlation.new(AEDB.CEA.matrix.plasma.RANK, method = "spearman", histogram = TRUE, pch = 3)
# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")
# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables
library(GGally)
# From the help page:
ggpairs(AEDB.CEA,
columns = c("MCP1_rank", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"),
columnLabels = c("MCP1 (plasma)",
"Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density",
"Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
method = c("spearman"),
# ggplot2::aes(colour = Gender),
progress = FALSE) Additional figures
Age and sex
We want to create per-age-group figures.
- Box and Whisker plot for MCP-1 plaque levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
- Box and Whisker plot for MCP-1 plasma levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
- Scatter plot of the correlation and regression line between MCP-1 levels in plaque (y axis) and plasma (x axis).
library(dplyr)
AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroup = factor(case_when(Age < 55 ~ "<55",
Age >= 55 & Age <= 64 ~ "55-64",
Age >= 65 & Age <= 74 ~ "65-74",
Age >= 75 & Age <= 84 ~ "75-84",
Age >= 85 ~ "85+")))
AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroupSex = factor(case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
Age >= 55 & Age <= 64 & Gender == "male"~ "55-64 males",
Age >= 65 & Age <= 74 & Gender == "male"~ "65-74 males",
Age >= 75 & Age <= 84 & Gender == "male"~ "75-84 males",
Age >= 85 & Gender == "male"~ "85+ males",
Age < 55 & Gender == "female" ~ "<55 females" ,
Age >= 55 & Age <= 64 & Gender == "female"~ "55-64 females ",
Age >= 65 & Age <= 74 & Gender == "female"~ "65-74 females",
Age >= 75 & Age <= 84 & Gender == "female"~ "75-84 females",
Age >= 85 & Gender == "female"~ "85+ females")))
table(AEDB.CEA$AgeGroup, AEDB.CEA$Gender)
female male
<55 45 98
55-64 194 410
65-74 264 687
75-84 202 439
85+ 34 50table(AEDB.CEA$AgeGroupSex)
<55 females <55 males 55-64 females 55-64 males 65-74 females 65-74 males 75-84 females 75-84 males 85+ females 85+ males
45 98 194 410 264 687 202 439 34 50 Now we can draw some graphs of plasma/plaque MCP1 levels per sex and age group.
Inverse-rank transformed data
MCP1 plaque levels
# ?ggpubr::ggboxplot()
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")MCP1 plasma levels
NOT AVAILABLE YET
# compare_means(MCP1_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1_rank",
xlab = "Gender",
ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_rank",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
Raw data
Simalarly but now for the raw data as median ± interquartile range.
MCP1 plaque levels
# ?ggpubr::ggboxplot()
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")MCP1 plasma levels
NOT AVAILABLE YET
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1",
xlab = "Gender",
ylab = "MCP1 plasma [pg/mL]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter"))
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plasma [pg/mL]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter"))Hypertension & blood pressure
We want to create figures of MCP1 levels stratified by hypertension/blood pressure, and use of anti-hypertensive drugs.
- Box and Whisker plot for MCP-1 plaque levels by hypertension group (no, yes)
- Box and Whisker plot for MCP-1 plasma levels by systolic blood pressure group (<120, 120-139, 140-159,160+)
library(dplyr)
AEDB.CEA <- AEDB.CEA %>% mutate(SBPGroup = factor(case_when(systolic < 120 ~ "<120",
systolic >= 120 & systolic <= 139 ~ "120-139",
systolic >= 140 & systolic <= 159 ~ "140-159",
systolic >= 160 ~ "160+")))
table(AEDB.CEA$SBPGroup, AEDB.CEA$Gender)
female male
<120 54 114
120-139 145 326
140-159 197 497
160+ 269 548Now we can draw some graphs of plasma/plaque MCP1 levels per sex and hypertension/blood pressure group.
Inverse-rank transformed data
MCP1 plaque levels
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup)),
x = c("SBPGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Systolic blood pressure (mmHg) per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)),
x = c("Hypertension.selfreport"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Self-reported hypertension per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)),
x = c("Hypertension.selfreport"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Self-reported hypertension per medication use",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Hypertension.drugs",
palette = c("#49A01D", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
MCP1 plasma levels
NOT AVAILABLE YET
ggpubr::ggboxplot(AEDB.CEA,
x = c("SBPGroup"),
y = "MCP1_rank",
xlab = "Systolic blood pressure (mmHg) per gender",
ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter")
Raw data
Simalarly but now for the raw data as median ± interquartile range.
MCP1 plaque levels
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup)),
x = c("SBPGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Systolic blood pressure (mmHg) per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)),
x = c("Hypertension.selfreport"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Self-reported hypertension per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)),
x = c("Hypertension.selfreport"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Self-reported hypertension per medication use",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Hypertension.drugs",
palette = c("#49A01D", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
MCP1 plasma levels
NOT AVAILABLE YET
ggpubr::ggboxplot(AEDB.CEA,
x = c("SBPGroup"),
y = "MCP1",
xlab = "Systolic blood pressure (mmHg) per gender",
ylab = "MCP1 plasma [pg/mL]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter"))Hypercholesterolemia & LDL levels
We want to create figures of MCP1 levels stratified by hypercholesterolemia/LDL-levels, and use of lipid-lowering drugs.
- Box and Whisker plot for MCP-1 plaque levels by hypercholesterolemia group (no, yes)
- Box and Whisker plot for MCP-1 plasma levels by LDL-levels (mmol/L) group (<100, 100-129, 130-159, 160-189, 190+)
library(dplyr)
AEDB.CEA <- AEDB.CEA %>% mutate(LDLGroup = factor(case_when(LDL_finalCU < 100 ~ "<100",
LDL_finalCU >= 100 & LDL_finalCU <= 129 ~ "100-129",
LDL_finalCU >= 130 & LDL_finalCU <= 159 ~ "130-159",
LDL_finalCU >= 160 & LDL_finalCU <= 189 ~ "160-189",
LDL_finalCU >= 190 ~ "190+")))
table(AEDB.CEA$LDLGroup, AEDB.CEA$Gender)
female male
<100 171 441
100-129 96 250
130-159 75 129
160-189 40 50
190+ 25 31Now we can draw some graphs of plasma/plaque MCP1 levels per sex and hypercholesterolemia/LDL-levels group, as well as stratified by lipid-lowering drugs users.
Inverse-rank transformed data
MCP1 plaque levels
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup)),
x = c("LDLGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "LDL (mg/dL) per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)),
x = c("LDLGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "LDL (mg/dL) per LLD use",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Med.Statin.LLD",
palette = c("#49A01D", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
MCP1 plasma levels
NOT AVAILABLE YET
# compare_means(MCP1_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1_rank",
xlab = "Gender",
ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_rank",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
Raw data
Simalarly but now for the raw data as median ± interquartile range.
MCP1 plaque levels
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup)),
x = c("LDLGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "LDL (mg/dL) per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)),
x = c("LDLGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "LDL (mg/dL) per LLD use",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Med.Statin.LLD",
palette = c("#49A01D", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
MCP1 plasma levels
NOT AVAILABLE YET
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1",
xlab = "Gender",
ylab = "MCP1 plasma [pg/mL]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter"))
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plasma [pg/mL]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter"))Kidney function (eGFR)
We want to create figures of MCP1 levels stratified by kidney function.
- Box and Whisker plot for MCP-1 plaque levels by chronic kidney disease (CKD) group (1, 2, 3, 4, 5)
- Box and Whisker plot for MCP-1 plasma levels by eGFR (MDRD-based) group (90+, 60-89, 30-59, <30)
library(dplyr)
AEDB.CEA <- AEDB.CEA %>% mutate(eGFRGroup = factor(case_when(GFR_MDRD < 15 ~ "<15",
GFR_MDRD >= 15 & GFR_MDRD <= 29 ~ "15-29",
GFR_MDRD >= 30 & GFR_MDRD <= 59 ~ "30-59",
GFR_MDRD >= 60 & GFR_MDRD <= 89 ~ "60-89",
GFR_MDRD >= 90 ~ "90+")))
table(AEDB.CEA$eGFRGroup, AEDB.CEA$Gender)
female male
<15 3 7
15-29 7 20
30-59 193 325
60-89 361 845
90+ 117 345table(AEDB.CEA$eGFRGroup, AEDB.CEA$KDOQI)
No data available/missing Normal kidney function CKD 2 (Mild) CKD 3 (Moderate) CKD 4 (Severe) CKD 5 (Failure)
<15 0 0 0 0 0 10
15-29 0 0 0 0 27 0
30-59 0 0 0 518 0 0
60-89 0 0 1206 0 0 0
90+ 0 462 0 0 0 0Now we can draw some graphs of plasma/plaque MCP1 levels per sex and kidney function group.
Inverse-rank transformed data
MCP1 plaque levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup)),
x = c("eGFRGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "eGFR (mL/min per 1.73 m2) per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(KDOQI)),
x = c("KDOQI"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Kidney function (KDOQI) per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right")
rm(p1)
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)),
x = c("eGFRGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "KDOQI",
palette = "npg",
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
MCP1 plasma levels
NOT AVAILABLE YET
# compare_means(MCP1_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1_rank",
xlab = "Gender",
ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_rank",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
Raw data
Simalarly but now for the raw data as median ± interquartile range.
MCP1 plaque levels
# ?ggpubr::ggboxplot()
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup)),
x = c("eGFRGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "eGFR (mL/min per 1.73 m2) per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(KDOQI)),
x = c("KDOQI"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Kidney function (KDOQI) per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)),
x = c("eGFRGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "KDOQI",
palette = "npg",
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
MCP1 plasma levels
NOT AVAILABLE YET
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1",
xlab = "Gender",
ylab = "MCP1 plasma [pg/mL]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter"))
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plasma [pg/mL]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter"))BMI
We want to create figures of MCP1 levels stratified by BMI.
- Box and Whisker plot for MCP-1 plaque levels by BMI WHO group (underweight, normal, overweight, obese)
- Box and Whisker plot for MCP-1 plasma levels by BMI group (<18.5, 18.5-24.9, 25, 29.9, 30-24.9, 35+)
library(dplyr)
AEDB.CEA <- AEDB.CEA %>% mutate(BMIGroup = factor(case_when(BMI < 18.5 ~ "<18.5",
BMI >= 18.5 & BMI < 25 ~ "18.5-24",
BMI >= 25 & BMI < 30 ~ "25-29",
BMI >= 30 & BMI < 35 ~ "30-35",
BMI >= 35 ~ "35+")))
# require(labelled)
# AEDB.CEA$BMI_US <- as_factor(AEDB.CEA$BMI_US)
# AEDB.CEA$BMI_WHO <- as_factor(AEDB.CEA$BMI_WHO)
# table(AEDB.CEA$BMI_WHO, AEDB.CEA$BMI_US)
table(AEDB.CEA$BMIGroup, AEDB.CEA$Gender)
female male
<18.5 17 8
18.5-24 277 574
25-29 267 786
30-35 99 189
35+ 32 32table(AEDB.CEA$BMIGroup, AEDB.CEA$BMI_WHO)
No data available/missing Underweight Normal Overweight Obese
<18.5 0 24 0 0 0
18.5-24 0 0 851 0 0
25-29 0 0 0 1052 0
30-35 0 0 0 0 288
35+ 0 0 0 0 64Now we can draw some graphs of plasma/plaque MCP1 levels per sex and age group.
Inverse-rank transformed data
MCP1 plaque levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "BMI groups (kg/m2)",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "BMI groups (kg/m2)",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)),
x = c("AgeGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "BMI groups (kg/m2) per WHO categories",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "BMI_WHO",
palette = "npg",
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
MCP1 plasma levels
NOT AVAILABLE YET
# compare_means(MCP1_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1_rank",
xlab = "Gender",
ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_rank",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
Raw data
Simalarly but now for the raw data as median ± interquartile range.
MCP1 plaque levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "BMI groups (kg/m2)",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "BMI groups (kg/m2)",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)),
x = c("AgeGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "BMI groups (kg/m2) per WHO categories",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "BMI_WHO",
palette = "npg",
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
MCP1 plasma levels
NOT AVAILABLE YET
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1",
xlab = "Gender",
ylab = "MCP1 plasma [pg/mL]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter"))
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plasma [pg/mL]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter"))Diabetes
We want to create figures of MCP1 levels stratified by type 2 diabetes.
- Box and Whisker plot for MCP-1 plaque levels by type 2 diabetes group (no, yes)
Now we can draw some graphs of plasma/plaque MCP1 levels per sex and age group.
Inverse-rank transformed data
MCP1 plaque levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Diabetes status",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Diabetes status per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")MCP1 plasma levels
NOT AVAILABLE YET
# compare_means(MCP1_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1_rank",
xlab = "Gender",
ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_rank",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
Raw data
Simalarly but now for the raw data as median ± interquartile range.
MCP1 plaque levels
# ?ggpubr::ggboxplot()
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Diabetes status",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Diabetes status per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")MCP1 plasma levels
NOT AVAILABLE YET
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1",
xlab = "Gender",
ylab = "MCP1 plasma [pg/mL]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter"))
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plasma [pg/mL]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter"))Smoking
We want to create figures of MCP1 levels stratified by smoking.
- Box and Whisker plot for MCP-1 plaque levels by smoking group (never, ex, current)
Now we can draw some graphs of plasma/plaque MCP1 levels per sex and age group.
Inverse-rank transformed data
MCP1 plaque levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Smoker status",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Smoker status per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")MCP1 plasma levels
NOT AVAILABLE YET
# compare_means(MCP1_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1_rank",
xlab = "Gender",
ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_rank",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
Raw data
Simalarly but now for the raw data as median ± interquartile range.
MCP1 plaque levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Smoker status",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Smoker status per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")MCP1 plasma levels
NOT AVAILABLE YET
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1",
xlab = "Gender",
ylab = "MCP1 plasma [pg/mL]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter"))
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plasma [pg/mL]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter"))Stenosis
We want to create figures of MCP1 levels stratified by stenosis grade.
- Box and Whisker plot for MCP-1 plaque levels by stenosis grade group (<70, 70-89, 90+)
library(dplyr)
AEDB.CEA <- AEDB.CEA %>% mutate(StenoticGroup = factor(case_when(stenose == "0-49%" ~ "<70",
stenose == "0-49%" ~ "<70",
stenose == "50-70%" ~ "<70",
stenose == "70-90%" ~ "70-89",
stenose == "50-99%" ~ "90+",
stenose == "70-99%" ~ "90+",
stenose == "100% (Occlusion)" ~ "90+",
stenose == "90-99%" ~ "90+")))
table(AEDB.CEA$StenoticGroup, AEDB.CEA$Gender)
female male
<70 46 157
70-89 365 762
90+ 316 726table(AEDB.CEA$stenose, AEDB.CEA$StenoticGroup)
<70 70-89 90+
missing 0 0 0
0-49% 13 0 0
50-70% 190 0 0
70-90% 0 1127 0
90-99% 0 0 928
100% (Occlusion) 0 0 31
NA 0 0 0
50-99% 0 0 15
70-99% 0 0 68
99 0 0 0Now we can draw some graphs of plasma/plaque MCP1 levels per sex and age group.
Inverse-rank transformed data
MCP1 plaque levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Stenotic grade",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Stenotic grade per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
MCP1 plasma levels
NOT AVAILABLE YET
# compare_means(MCP1_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1_rank",
xlab = "Gender",
ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_rank",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
Raw data
Simalarly but now for the raw data as median ± interquartile range.
MCP1 plaque levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Stenotic grade",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Stenotic grade per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
MCP1 plasma levels
NOT AVAILABLE YET
ggpubr::ggboxplot(AEDB.CEA,
x = c("Gender"),
y = "MCP1",
xlab = "Gender",
ylab = "MCP1 plasma [pg/mL]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter"))
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plasma [pg/mL]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter"))Circulating vs. plaque MCP1 levels
We will also make a nice correlation plot between plasma and plaque MCP1 levels.
NOT AVAILABLE YET
ggpubr::ggscatter(AEDB.CEA,
x = "MCP1_pg_ug_2015",
y = "MCP1",
xlab = "MCP1 plaque [pg/ug]",
ylab = "MCP1 plasma [pg/mL]",
add = "reg.line", add.params = list(color = "#1290D9"),
conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(8,750))
ggpubr::ggscatter(AEDB.CEA,
x = "MCP1_pg_ug_2015_rank",
y = "MCP1_rank",
xlab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
add = "reg.line", add.params = list(color = "#1290D9"),
conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(2,3))
Plaque vs. plaque MCP1 levels
We will also make a nice correlation plot between the two experiments of plaque MCP1 levels.
AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
summary(AEDB.CEA$MCP1)
summary(AEDB.CEA$MCP1_pg_ug_2015)
ggpubr::ggscatter(AEDB.CEA,
x = "MCP1",
y = "MCP1_pg_ug_2015",
xlab = "MCP1 plaque [pg/mL] (exp. no. 1)",
ylab = "MCP1 plaque [pg/ug] (exp. no. 2)",
add = "reg.line", add.params = list(color = "#1290D9"),
conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method = "spearman"))
ggpubr::ggscatter(AEDB.CEA,
x = "MCP1_rank",
y = "MCP1_pg_ug_2015_rank",
xlab = "MCP1 plaque [pg/mL]\n(INRT, exp. no. 1)",
ylab = "MCP1 plaque [pg/ug]\n(INRT, exp. no. 2)",
add = "reg.line", add.params = list(color = "#1290D9"),
conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method = "spearman"))
Symptoms
We want to create per-symptom figures.
library(dplyr)
table(AEDB.CEA$AgeGroup, AEDB.CEA$AsymptSympt2G)
Asymptomatic Symptomatic
<55 24 119
55-64 76 528
65-74 124 827
75-84 43 598
85+ 3 81table(AEDB.CEA$Gender, AEDB.CEA$AsymptSympt2G)
Asymptomatic Symptomatic
female 64 675
male 206 1478table(AEDB.CEA$AsymptSympt2G)
Asymptomatic Symptomatic
270 2153 Now we can draw some graphs of plasma/plaque MCP1 levels per symptom group.
# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))
p1 <- ggpubr::ggboxplot(AEDB.CEA,
x = "AsymptSympt2G", y = "MCP1_pg_ug_2015_rank",
title = "MCP1 plaque [pg/ug] levels per symptom",
xlab = "Symptoms",
ylab = "MCP1 plaque [pg/ug]\n inverse-rank transformation",
color = "AsymptSympt2G",
palette = c(uithof_color[16], uithof_color[23]),
add = "dotplot", # Add dotplot
add.params = list(binwidth = 0.1, dotsize = 0.3)
) +
stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
p1 <- ggpubr::ggboxplot(AEDB.CEA,
x = "AsymptSympt2G", y = "MCP1_rank",
title = "MCP1 plasma [pg/mL] levels per symptom",
xlab = "Symptoms",
ylab = "MCP1 plasma [pg/mL]\n inverse-rank transformation",
color = "AsymptSympt2G",
palette = c(uithof_color[16], uithof_color[23]),
add = "dotplot", # Add dotplot
add.params = list(binwidth = 0.1, dotsize = 0.3)
) +
stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
rm(p1)Forest plots
We would also like to visualize the multivariable analyses results.
library(ggplot2)
library(openxlsx)
model1_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"))
model2_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"))
model1_mcp1$model <- "univariate"
model2_mcp1$model <- "multivariate"
models_mcp1 <- rbind(model1_mcp1, model2_mcp1)
models_mcp1NA
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"),
levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_rank"]),
low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_rank"]),
high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_rank"]))
fp <- ggplot(data=dat, aes(x=group, y=cen, ymin=low, ymax=high)) +
geom_pointrange() +
geom_hline(yintercept=1, lty=2) + # add a dotted line at x=1 after flip
coord_flip() + # flip coordinates (puts labels on y axis)
xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
theme(text = element_text(size=14)) +
ggtitle("plasma MCP-1 levels (1 SD increment)") +
theme_minimal() # use a white background
print(fp)
rm(fp)MCP1 vs. cytokines plaque levels correlations
We will plot the correlations of other cytokine plaque levels to the MCP1 plaque levels. These include:
- IL2
- IL4
- IL5
- IL6
- IL8
- IL9
- IL10
- IL12
- IL13
- IL21
- INFG
- TNFA
- MIF
- MCP1
- MIP1a
- RANTES
- MIG
- IP10
- Eotaxin1
- TARC
- PARC
- MDC
- OPG
- sICAM1
- VEGFA
- TGFB
In addition we will look at three metalloproteinases which were measured using an activity assay.
- MMP2
- MMP8
- MMP9
The proteins were measured using FACS and LUMINEX. Given the different platforms used (FACS vs. LUMINEX), we will inverse rank-normalize these variables as well to scale them to the same scale as the MCP1 plaque levels.
We will set the measurements that yielded ‘0’ to NA, as it is unlikely that any protein ever has exactly 0 copies. The ‘0’ yielded during the experiment are due to the limits of the detection.
Prepare data
cytokines <- c("IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", "IL13", "IL21",
"INFG", "TNFA", "MIF", "MCP1", "MIP1a", "RANTES", "MIG", "IP10", "Eotaxin1",
"TARC", "PARC", "MDC", "OPG", "sICAM1", "VEGFA", "TGFB")
metalloproteinases <- c("MMP2", "MMP8", "MMP9")
# fix names
names(AEDB.CEA)[names(AEDB.CEA) == "VEFGA"] <- "VEGFA"
proteins_of_interest <- c(cytokines, metalloproteinases)
proteins_of_interest_rank = unlist(lapply(proteins_of_interest, paste0, "_rank"))
# make variables numerics()
AEDB.CEA <- AEDB.CEA %>%
mutate_each(funs(as.numeric), proteins_of_interest)
for(PROTEIN in 1:length(proteins_of_interest)){
# UCORBIOGSAqc$Z <- NULL
var.temp.rank = proteins_of_interest_rank[PROTEIN]
var.temp = proteins_of_interest[PROTEIN]
cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.rank,".\n"))
cat(paste0("* changing ", var.temp, " to numeric.\n"))
# AEDB.CEA <- AEDB.CEA %>% mutate(AEDB.CEA[,var.temp] == replace(AEDB.CEA[,var.temp], AEDB.CEA[,var.temp]==0, NA))
AEDB.CEA[,var.temp][AEDB.CEA[,var.temp]==0.000000]=NA
cat(paste0("* standardising ", var.temp,
" (mean: ",round(mean(!is.na(AEDB.CEA[,var.temp])), digits = 6),
", n = ",sum(!is.na(AEDB.CEA[,var.temp])),").\n"))
AEDB.CEA <- AEDB.CEA %>%
mutate_at(vars(var.temp),
# list(Z = ~ (AEDB.CEA[,var.temp] - mean(AEDB.CEA[,var.temp], na.rm = TRUE))/sd(AEDB.CEA[,var.temp], na.rm = TRUE))
list(RANK = ~ qnorm((rank(AEDB.CEA[,var.temp], na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA[,var.temp]))))
)
# str(UCORBIOGSAqc$Z)
cat(paste0("* renaming RANK to ", var.temp.rank,".\n"))
AEDB.CEA[,var.temp.rank] <- NULL
names(AEDB.CEA)[names(AEDB.CEA) == "RANK"] <- var.temp.rank
}
Selecting IL2 and standardising: IL2_rank.
* changing IL2 to numeric.
* standardising IL2 (mean: 0.179942, n = 436).
* renaming RANK to IL2_rank.
Selecting IL4 and standardising: IL4_rank.
* changing IL4 to numeric.
* standardising IL4 (mean: 0.167561, n = 406).
* renaming RANK to IL4_rank.
Selecting IL5 and standardising: IL5_rank.
* changing IL5 to numeric.
* standardising IL5 (mean: 0.178291, n = 432).
* renaming RANK to IL5_rank.
Selecting IL6 and standardising: IL6_rank.
* changing IL6 to numeric.
* standardising IL6 (mean: 0.188196, n = 456).
* renaming RANK to IL6_rank.
Selecting IL8 and standardising: IL8_rank.
* changing IL8 to numeric.
* standardising IL8 (mean: 0.182006, n = 441).
* renaming RANK to IL8_rank.
Selecting IL9 and standardising: IL9_rank.
* changing IL9 to numeric.
* standardising IL9 (mean: 0.206356, n = 500).
* renaming RANK to IL9_rank.
Selecting IL10 and standardising: IL10_rank.
* changing IL10 to numeric.
* standardising IL10 (mean: 0.158894, n = 385).
* renaming RANK to IL10_rank.
Selecting IL12 and standardising: IL12_rank.
* changing IL12 to numeric.
* standardising IL12 (mean: 0.167974, n = 407).
* renaming RANK to IL12_rank.
Selecting IL13 and standardising: IL13_rank.
* changing IL13 to numeric.
* standardising IL13 (mean: 0.232769, n = 564).
* renaming RANK to IL13_rank.
Selecting IL21 and standardising: IL21_rank.
* changing IL21 to numeric.
* standardising IL21 (mean: 0.233182, n = 565).
* renaming RANK to IL21_rank.
Selecting INFG and standardising: INFG_rank.
* changing INFG to numeric.
* standardising INFG (mean: 0.179117, n = 434).
* renaming RANK to INFG_rank.
Selecting TNFA and standardising: TNFA_rank.
* changing TNFA to numeric.
* standardising TNFA (mean: 0.163434, n = 396).
* renaming RANK to TNFA_rank.
Selecting MIF and standardising: MIF_rank.
* changing MIF to numeric.
* standardising MIF (mean: 0.233182, n = 565).
* renaming RANK to MIF_rank.
Selecting MCP1 and standardising: MCP1_rank.
* changing MCP1 to numeric.
* standardising MCP1 (mean: 0.229468, n = 556).
* renaming RANK to MCP1_rank.
Selecting MIP1a and standardising: MIP1a_rank.
* changing MIP1a to numeric.
* standardising MIP1a (mean: 0.211721, n = 513).
* renaming RANK to MIP1a_rank.
Selecting RANTES and standardising: RANTES_rank.
* changing RANTES to numeric.
* standardising RANTES (mean: 0.228642, n = 554).
* renaming RANK to RANTES_rank.
Selecting MIG and standardising: MIG_rank.
* changing MIG to numeric.
* standardising MIG (mean: 0.226991, n = 550).
* renaming RANK to MIG_rank.
Selecting IP10 and standardising: IP10_rank.
* changing IP10 to numeric.
* standardising IP10 (mean: 0.205943, n = 499).
* renaming RANK to IP10_rank.
Selecting Eotaxin1 and standardising: Eotaxin1_rank.
* changing Eotaxin1 to numeric.
* standardising Eotaxin1 (mean: 0.233182, n = 565).
* renaming RANK to Eotaxin1_rank.
Selecting TARC and standardising: TARC_rank.
* changing TARC to numeric.
* standardising TARC (mean: 0.200578, n = 486).
* renaming RANK to TARC_rank.
Selecting PARC and standardising: PARC_rank.
* changing PARC to numeric.
* standardising PARC (mean: 0.233182, n = 565).
* renaming RANK to PARC_rank.
Selecting MDC and standardising: MDC_rank.
* changing MDC to numeric.
* standardising MDC (mean: 0.209657, n = 508).
* renaming RANK to MDC_rank.
Selecting OPG and standardising: OPG_rank.
* changing OPG to numeric.
* standardising OPG (mean: 0.232769, n = 564).
* renaming RANK to OPG_rank.
Selecting sICAM1 and standardising: sICAM1_rank.
* changing sICAM1 to numeric.
* standardising sICAM1 (mean: 0.233182, n = 565).
* renaming RANK to sICAM1_rank.
Selecting VEGFA and standardising: VEGFA_rank.
* changing VEGFA to numeric.
* standardising VEGFA (mean: 0.201403, n = 488).
* renaming RANK to VEGFA_rank.
Selecting TGFB and standardising: TGFB_rank.
* changing TGFB to numeric.
* standardising TGFB (mean: 0.22988, n = 557).
* renaming RANK to TGFB_rank.
Selecting MMP2 and standardising: MMP2_rank.
* changing MMP2 to numeric.
* standardising MMP2 (mean: 0.231944, n = 562).
* renaming RANK to MMP2_rank.
Selecting MMP8 and standardising: MMP8_rank.
* changing MMP8 to numeric.
* standardising MMP8 (mean: 0.231944, n = 562).
* renaming RANK to MMP8_rank.
Selecting MMP9 and standardising: MMP9_rank.
* changing MMP9 to numeric.
* standardising MMP9 (mean: 0.231531, n = 561).
* renaming RANK to MMP9_rank.# rm(var.temp, var.temp.rank)
Visualize transformations
We will just visualize these transformations.
proteins_of_interest_rank_mcp1 <- c("MCP1_pg_ug_2015_rank", "MCP1_pg_ml_2015_rank", proteins_of_interest_rank)
proteins_of_interest_mcp1 <- c("MCP1_pg_ug_2015", "MCP1_pg_ml_2015", proteins_of_interest)
for(PROTEIN in proteins_of_interest_mcp1){
cat(paste0("Plotting protein ", PROTEIN, ".\n"))
p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = paste0(PROTEIN, " plaque levels"),
xlab = "",
ggtheme = theme_minimal())
print(p1)
}Plotting protein MCP1_pg_ug_2015.Using `bins = 30` by default. Pick better value with the argument `bins`.Plotting protein MCP1_pg_ml_2015.
Plotting protein IL2.
Plotting protein IL4.
Plotting protein IL5.
Plotting protein IL6.
Plotting protein IL8.
Plotting protein IL9.
Plotting protein IL10.
Plotting protein IL12.
Plotting protein IL13.
Plotting protein IL21.
Plotting protein INFG.
Plotting protein TNFA.
Plotting protein MIF.
Plotting protein MCP1.
Plotting protein MIP1a.
Plotting protein RANTES.
Plotting protein MIG.
Plotting protein IP10.
Plotting protein Eotaxin1.
Plotting protein TARC.
Plotting protein PARC.
Plotting protein MDC.
Plotting protein OPG.
Plotting protein sICAM1.
Plotting protein VEGFA.
Plotting protein TGFB.
Plotting protein MMP2.
Plotting protein MMP8.
Plotting protein MMP9.
for(PROTEIN in proteins_of_interest_rank_mcp1){
cat(paste0("Plotting protein ", PROTEIN, ".\n"))
p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = paste0(PROTEIN, " plaque levels"),
xlab = "inverse-normal transformation",
ggtheme = theme_minimal())
print(p1)
}Plotting protein MCP1_pg_ug_2015_rank.Using `bins = 30` by default. Pick better value with the argument `bins`.Plotting protein MCP1_pg_ml_2015_rank.
Plotting protein IL2_rank.
Plotting protein IL4_rank.
Plotting protein IL5_rank.
Plotting protein IL6_rank.
Plotting protein IL8_rank.
Plotting protein IL9_rank.
Plotting protein IL10_rank.
Plotting protein IL12_rank.
Plotting protein IL13_rank.
Plotting protein IL21_rank.
Plotting protein INFG_rank.
Plotting protein TNFA_rank.
Plotting protein MIF_rank.
Plotting protein MCP1_rank.
Plotting protein MIP1a_rank.
Plotting protein RANTES_rank.
Plotting protein MIG_rank.
Plotting protein IP10_rank.
Plotting protein Eotaxin1_rank.
Plotting protein TARC_rank.
Plotting protein PARC_rank.
Plotting protein MDC_rank.
Plotting protein OPG_rank.
Plotting protein sICAM1_rank.
Plotting protein VEGFA_rank.
Plotting protein TGFB_rank.
Plotting protein MMP2_rank.
Plotting protein MMP8_rank.
Plotting protein MMP9_rank.
NACorrelations
Here we calculate correlations between MCP1_pg_ug_2015 and 28 other cytokines (including MCP1 as measured in experiment 1. We use Spearman’s test, thus, correlations a given in rho. Please note the indications of measurement methods:
- L: LUMINEX
- E: ELISA
- a: activity assay
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools))
install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")Skipping install of 'ggcorrplot' from a github remote, the SHA1 (c46b4cce) has not changed since last install.
Use `force = TRUE` to force installationlibrary(ggcorrplot)
# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA,
select = c(proteins_of_interest_rank_mcp1)
)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)
corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK,
use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
method = "spearman"), 3)
# corr_biomarkers.rank
rename_proteins_of_interest_mcp1 <- c("MCP1 (L, exp2, pg/ug)", "MCP1 (L, exp2, pg/mL)",
"IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12",
"IL13 (L)", "IL21 (L)",
"INFG", "TNFA", "MIF (L)",
"MCP1 (L, exp1)", "MIP1a (L)", "RANTES (L)", "MIG (L)", "IP10 (L)",
"Eotaxin1 (L)", "TARC (L)", "PARC (L)", "MDC (L)",
"OPG (L)", "sICAM1 (L)", "VEGFA (E)", "TGFB (E)", "MMP2 (a)", "MMP8 (a)", "MMP9 (a)")
colnames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)
rownames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)
corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")Cannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with ties# ++++++++++++++++++++++++++++
# flattenCorrMatrix
# ++++++++++++++++++++++++++++
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
ut <- upper.tri(cormat)
data.frame(
row = rownames(cormat)[row(cormat)[ut]],
column = rownames(cormat)[col(cormat)[ut]],
cor =(cormat)[ut],
p = pmat[ut]
)
}
corr_biomarkers.rank.df <- flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank)
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "row"] <- "Cytokine_X"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "column"] <- "CytokineY"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "cor"] <- "SpearmanRho"
DT::datatable(corr_biomarkers.rank.df)
fwrite(corr_biomarkers.rank.df, file = paste0(OUT_loc, "/",Today,".correlation_cytokines.txt"))
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
p1 <- ggcorrplot(corr_biomarkers.rank,
method = "square",
type = "lower",
title = "Cross biomarker correlations",
show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
show.diag = TRUE,
hc.order = FALSE,
lab = FALSE,
digits = 3,
tl.cex = 6,
# xlab = c("MCP1"),
# p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
colors = c("#1290D9", "#FFFFFF", "#E55738"))
p1
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.png"), plot = last_plot())Saving 7.29 x 4.51 in image
rm(p1)
While visually actractive we are not necessarily interested in the correlations between all the cytokines, rather of MCP1 with other cytokines only.
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "MCP1 (L, exp2, pg/ug)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/29)
p_threshold[1] 2.763428p1 <- ggbarplot(temp, x = "CytokineY", y = "SpearmanRho",
fill = "CytokineY", # change fill color by cyl
# color = "white", # Set bar border colors to white
palette = uithof_color, # jco journal color palett. see ?ggpar
xlab = "Cytokine",
ylab = expression("Spearman's"~italic(rho)),
sort.val = "desc", # Sort the value in dscending order
sort.by.groups = FALSE, # Don't sort inside each group
x.text.angle = 45, # Rotate vertically x axis texts
cex = 0.8
)
ggpar(p1, legend = "bottom",
legend.title = "") +
theme(axis.text.x = element_text(size = 9),
axis.text.y = element_text(size = 9)) 
rm(p1)
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "MCP1 (L, exp2, pg/mL)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/29)
p_threshold[1] 2.763428p1 <- ggbarplot(temp, x = "CytokineY", y = "SpearmanRho",
fill = "CytokineY", # change fill color by cyl
# color = "white", # Set bar border colors to white
palette = uithof_color, # jco journal color palett. see ?ggpar
xlab = "Cytokine",
ylab = expression("Spearman's"~italic(rho)),
sort.val = "desc", # Sort the value in dscending order
sort.by.groups = FALSE, # Don't sort inside each group
x.text.angle = 45, # Rotate vertically x axis texts
cex = 0.8
)
ggpar(p1, legend = "bottom",
legend.title = "") +
theme(axis.text.x = element_text(size = 9),
axis.text.y = element_text(size = 9)) 
rm(p1)
Another version - problably not good.
p1 <- ggdotchart(temp, x = "CytokineY", y = "p_log10",
color = "CytokineY", # Color by groups
palette = uithof_color, # Custom color palette
xlab = "Cytokine",
ylab = expression(log[10]~"("~italic(p)~")-value"),
ylim = c(0, 6),
sorting = "descending", # Sort value in descending order
add = "segments", # Add segments from y = 0 to dots
rotate = FALSE, # Rotate vertically
# group = "CytokineY", # Order by groups
dot.size = 8, # Large dot size
label = round(temp$SpearmanRho, digits = 3), # Add mpg values as dot labels
font.label = list(color = "white", size = 8,
vjust = 0.5)
)
ggpar(p1, legend = "bottom",
legend.title = "") +
theme(axis.text.x = element_text(size = 9),
axis.text.y = element_text(size = 9))
# rm(temp, p1)
MCP1 vs. cytokines plaque levels lm()
Model 1
In this model we correct for Age, Gender, and year of surgery.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of plaque cytokines traits as a function of plasma/plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(proteins_of_interest_rank)) {
TRAIT = proteins_of_interest_rank[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ug_2015_rank.
- processing IL2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.3620 -0.2628 -0.9081 -0.7780 1.8186
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9917 -0.6348 -0.1100 0.5457 2.9426
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.2345432 0.4484350 0.523 0.6013
currentDF[, TRAIT] 0.0330038 0.0562283 0.587 0.5576
Age 0.0008222 0.0061975 0.133 0.8945
Gendermale 0.0851576 0.1155898 0.737 0.4618
ORdate_year2003 -0.2431935 0.1700065 -1.430 0.1535
ORdate_year2004 -0.8902256 0.1625588 -5.476 8.57e-08 ***
ORdate_year2005 -0.7743076 0.1936880 -3.998 7.88e-05 ***
ORdate_year2006 1.8714130 1.0008787 1.870 0.0624 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9789 on 333 degrees of freedom
Multiple R-squared: 0.1373, Adjusted R-squared: 0.1192
F-statistic: 7.571 on 7 and 333 DF, p-value: 1.777e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL2_rank
Effect size...............: 0.033004
Standard error............: 0.056228
Odds ratio (effect size)..: 1.034
Lower 95% CI..............: 0.926
Upper 95% CI..............: 1.154
T-value...................: 0.586962
P-value...................: 0.5576271
R^2.......................: 0.137295
Adjusted r^2..............: 0.11916
Sample size of AE DB......: 2423
Sample size of model......: 341
Missing data %............: 85.92654
- processing IL4_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year2003 ORdate_year2004 ORdate_year2005
0.3763 -0.2485 -0.9434 -0.5053
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0490 -0.6159 -0.1179 0.5095 2.9794
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.136470 0.463291 0.295 0.7685
currentDF[, TRAIT] -0.026017 0.057918 -0.449 0.6536
Age 0.002894 0.006360 0.455 0.6494
Gendermale 0.082685 0.120841 0.684 0.4943
ORdate_year2003 -0.259649 0.172454 -1.506 0.1332
ORdate_year2004 -0.962345 0.164735 -5.842 1.31e-08 ***
ORdate_year2005 -0.529840 0.215163 -2.463 0.0143 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.966 on 309 degrees of freedom
Multiple R-squared: 0.1358, Adjusted R-squared: 0.119
F-statistic: 8.091 on 6 and 309 DF, p-value: 3.936e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL4_rank
Effect size...............: -0.026017
Standard error............: 0.057918
Odds ratio (effect size)..: 0.974
Lower 95% CI..............: 0.87
Upper 95% CI..............: 1.091
T-value...................: -0.449209
P-value...................: 0.653596
R^2.......................: 0.135778
Adjusted r^2..............: 0.118997
Sample size of AE DB......: 2423
Sample size of model......: 316
Missing data %............: 86.95832
- processing IL5_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year2003 ORdate_year2004 ORdate_year2005
0.4319 -0.3244 -0.9619 -0.6738
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0635 -0.6317 -0.1078 0.5099 3.0197
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.184316 0.433866 0.425 0.671242
currentDF[, TRAIT] -0.030098 0.054855 -0.549 0.583587
Age 0.002694 0.006037 0.446 0.655648
Gendermale 0.121202 0.113055 1.072 0.284471
ORdate_year2003 -0.339279 0.166269 -2.041 0.042089 *
ORdate_year2004 -0.989790 0.159578 -6.203 1.66e-09 ***
ORdate_year2005 -0.699931 0.196304 -3.566 0.000417 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9528 on 331 degrees of freedom
Multiple R-squared: 0.1348, Adjusted R-squared: 0.1191
F-statistic: 8.593 on 6 and 331 DF, p-value: 1.076e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL5_rank
Effect size...............: -0.030098
Standard error............: 0.054855
Odds ratio (effect size)..: 0.97
Lower 95% CI..............: 0.871
Upper 95% CI..............: 1.08
T-value...................: -0.548692
P-value...................: 0.5835868
R^2.......................: 0.134776
Adjusted r^2..............: 0.119093
Sample size of AE DB......: 2423
Sample size of model......: 338
Missing data %............: 86.05035
- processing IL6_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.43948 -0.32658 -0.97118 -1.18231 -0.07596
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0156 -0.6779 -0.1707 0.5293 3.1393
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.124550 0.459541 0.271 0.787
currentDF[, TRAIT] 0.072164 0.057987 1.244 0.214
Age 0.002668 0.006377 0.418 0.676
Gendermale 0.164109 0.117902 1.392 0.165
ORdate_year2003 -0.282647 0.174776 -1.617 0.107
ORdate_year2004 -0.937848 0.166781 -5.623 3.88e-08 ***
ORdate_year2005 -1.215982 0.192126 -6.329 7.69e-10 ***
ORdate_year2006 -0.083841 0.733381 -0.114 0.909
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.011 on 344 degrees of freedom
Multiple R-squared: 0.1618, Adjusted R-squared: 0.1447
F-statistic: 9.485 on 7 and 344 DF, p-value: 8.914e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL6_rank
Effect size...............: 0.072164
Standard error............: 0.057987
Odds ratio (effect size)..: 1.075
Lower 95% CI..............: 0.959
Upper 95% CI..............: 1.204
T-value...................: 1.244486
P-value...................: 0.214168
R^2.......................: 0.161789
Adjusted r^2..............: 0.144733
Sample size of AE DB......: 2423
Sample size of model......: 352
Missing data %............: 85.47255
- processing IL8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.5353 0.2208 -0.4198 -1.0477 -1.4484 -0.8770
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1247 -0.6432 -0.1477 0.5735 3.0446
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.684031 0.449340 1.522 0.128880
currentDF[, TRAIT] 0.213453 0.055726 3.830 0.000153 ***
Age -0.003752 0.006223 -0.603 0.546919
Gendermale 0.152850 0.117965 1.296 0.195967
ORdate_year2003 -0.424550 0.173906 -2.441 0.015155 *
ORdate_year2004 -1.056251 0.165515 -6.382 5.85e-10 ***
ORdate_year2005 -1.434840 0.185976 -7.715 1.41e-13 ***
ORdate_year2006 -0.913525 0.514097 -1.777 0.076486 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9855 on 334 degrees of freedom
Multiple R-squared: 0.2078, Adjusted R-squared: 0.1912
F-statistic: 12.51 on 7 and 334 DF, p-value: 2.979e-14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL8_rank
Effect size...............: 0.213453
Standard error............: 0.055726
Odds ratio (effect size)..: 1.238
Lower 95% CI..............: 1.11
Upper 95% CI..............: 1.381
T-value...................: 3.830438
P-value...................: 0.0001527729
R^2.......................: 0.207762
Adjusted r^2..............: 0.191158
Sample size of AE DB......: 2423
Sample size of model......: 342
Missing data %............: 85.88527
- processing IL9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.3860 0.1264 -0.2101 -0.9505 -1.0487 -0.6280
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1165 -0.7339 -0.1489 0.5609 2.9624
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.598668 0.457744 1.308 0.1917
currentDF[, TRAIT] 0.121234 0.055554 2.182 0.0297 *
Age -0.004582 0.006298 -0.727 0.4674
Gendermale 0.149585 0.116387 1.285 0.1995
ORdate_year2003 -0.222499 0.192296 -1.157 0.2480
ORdate_year2004 -0.963250 0.176131 -5.469 8.31e-08 ***
ORdate_year2005 -1.045247 0.176895 -5.909 7.77e-09 ***
ORdate_year2006 -0.634687 0.283809 -2.236 0.0259 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.046 on 373 degrees of freedom
Multiple R-squared: 0.1496, Adjusted R-squared: 0.1336
F-statistic: 9.37 on 7 and 373 DF, p-value: 1.034e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL9_rank
Effect size...............: 0.121234
Standard error............: 0.055554
Odds ratio (effect size)..: 1.129
Lower 95% CI..............: 1.012
Upper 95% CI..............: 1.259
T-value...................: 2.182261
P-value...................: 0.0297136
R^2.......................: 0.149553
Adjusted r^2..............: 0.133593
Sample size of AE DB......: 2423
Sample size of model......: 381
Missing data %............: 84.27569
- processing IL10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year2003 ORdate_year2004 ORdate_year2005
0.3016 -0.1677 -0.8636 -0.3192
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0704 -0.6223 -0.1270 0.4843 2.9980
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.050782 0.470356 -0.108 0.914
currentDF[, TRAIT] 0.018549 0.061645 0.301 0.764
Age 0.003554 0.006532 0.544 0.587
Gendermale 0.156951 0.124322 1.262 0.208
ORdate_year2003 -0.153510 0.175500 -0.875 0.382
ORdate_year2004 -0.862597 0.168345 -5.124 5.43e-07 ***
ORdate_year2005 -0.320473 0.244238 -1.312 0.190
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9796 on 294 degrees of freedom
Multiple R-squared: 0.1307, Adjusted R-squared: 0.113
F-statistic: 7.37 on 6 and 294 DF, p-value: 2.337e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL10_rank
Effect size...............: 0.018549
Standard error............: 0.061645
Odds ratio (effect size)..: 1.019
Lower 95% CI..............: 0.903
Upper 95% CI..............: 1.15
T-value...................: 0.300904
P-value...................: 0.7637
R^2.......................: 0.130748
Adjusted r^2..............: 0.113008
Sample size of AE DB......: 2423
Sample size of model......: 301
Missing data %............: 87.57738
- processing IL12_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year2003 ORdate_year2004 ORdate_year2005
0.3012 -0.1766 -0.8561 -0.6157
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0692 -0.6175 -0.1404 0.5207 2.9701
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.179640 0.466374 -0.385 0.70037
currentDF[, TRAIT] 0.046670 0.058746 0.794 0.42755
Age 0.005279 0.006453 0.818 0.41398
Gendermale 0.146949 0.119958 1.225 0.22150
ORdate_year2003 -0.130506 0.176741 -0.738 0.46083
ORdate_year2004 -0.836820 0.165671 -5.051 7.53e-07 ***
ORdate_year2005 -0.608259 0.221345 -2.748 0.00635 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9724 on 309 degrees of freedom
Multiple R-squared: 0.1275, Adjusted R-squared: 0.1106
F-statistic: 7.526 on 6 and 309 DF, p-value: 1.522e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL12_rank
Effect size...............: 0.04667
Standard error............: 0.058746
Odds ratio (effect size)..: 1.048
Lower 95% CI..............: 0.934
Upper 95% CI..............: 1.176
T-value...................: 0.794432
P-value...................: 0.4275535
R^2.......................: 0.127505
Adjusted r^2..............: 0.110563
Sample size of AE DB......: 2423
Sample size of model......: 316
Missing data %............: 86.95832
- processing IL13_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.4516 0.1536 -0.3551 -0.9984 -1.1330 -0.6781
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0029 -0.6944 -0.1587 0.5321 3.0112
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.556329 0.413448 1.346 0.1792
currentDF[, TRAIT] 0.148371 0.050988 2.910 0.0038 **
Age -0.002579 0.005680 -0.454 0.6500
Gendermale 0.103696 0.105241 0.985 0.3250
ORdate_year2003 -0.361074 0.176164 -2.050 0.0410 *
ORdate_year2004 -1.001193 0.168402 -5.945 5.75e-09 ***
ORdate_year2005 -1.129306 0.171826 -6.572 1.45e-10 ***
ORdate_year2006 -0.683258 0.273394 -2.499 0.0128 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.011 on 426 degrees of freedom
Multiple R-squared: 0.1478, Adjusted R-squared: 0.1338
F-statistic: 10.56 on 7 and 426 DF, p-value: 2.903e-12
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL13_rank
Effect size...............: 0.148371
Standard error............: 0.050988
Odds ratio (effect size)..: 1.16
Lower 95% CI..............: 1.05
Upper 95% CI..............: 1.282
T-value...................: 2.90994
P-value...................: 0.003804448
R^2.......................: 0.147842
Adjusted r^2..............: 0.13384
Sample size of AE DB......: 2423
Sample size of model......: 434
Missing data %............: 82.08832
- processing IL21_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.4408 0.1418 -0.3465 -0.9875 -1.1146 -0.6564
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0513 -0.6845 -0.1504 0.5563 2.9758
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.577740 0.412341 1.401 0.16190
currentDF[, TRAIT] 0.135053 0.051527 2.621 0.00908 **
Age -0.003027 0.005652 -0.536 0.59258
Gendermale 0.100466 0.105225 0.955 0.34023
ORdate_year2003 -0.352065 0.175682 -2.004 0.04570 *
ORdate_year2004 -0.989572 0.168289 -5.880 8.28e-09 ***
ORdate_year2005 -1.110432 0.171394 -6.479 2.55e-10 ***
ORdate_year2006 -0.660808 0.273738 -2.414 0.01620 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.012 on 427 degrees of freedom
Multiple R-squared: 0.1445, Adjusted R-squared: 0.1305
F-statistic: 10.31 on 7 and 427 DF, p-value: 5.852e-12
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL21_rank
Effect size...............: 0.135053
Standard error............: 0.051527
Odds ratio (effect size)..: 1.145
Lower 95% CI..............: 1.035
Upper 95% CI..............: 1.266
T-value...................: 2.621001
P-value...................: 0.00908008
R^2.......................: 0.144523
Adjusted r^2..............: 0.130498
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing INFG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.2618 0.1810 -0.2530 -0.9506 -0.9530 -1.6086
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0510 -0.6338 -0.1263 0.5110 2.9746
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.1688208 0.4596170 0.367 0.7136
currentDF[, TRAIT] 0.0604226 0.0624916 0.967 0.3343
Age 0.0007079 0.0063368 0.112 0.9111
Gendermale 0.1957171 0.1198107 1.634 0.1033
ORdate_year2003 -0.2188750 0.1734657 -1.262 0.2079
ORdate_year2004 -0.9158013 0.1660745 -5.514 7.10e-08 ***
ORdate_year2005 -0.8829951 0.2099986 -4.205 3.38e-05 ***
ORdate_year2006 -1.4818514 0.7304186 -2.029 0.0433 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.996 on 328 degrees of freedom
Multiple R-squared: 0.1503, Adjusted R-squared: 0.1322
F-statistic: 8.29 on 7 and 328 DF, p-value: 2.542e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: INFG_rank
Effect size...............: 0.060423
Standard error............: 0.062492
Odds ratio (effect size)..: 1.062
Lower 95% CI..............: 0.94
Upper 95% CI..............: 1.201
T-value...................: 0.96689
P-value...................: 0.3343113
R^2.......................: 0.150318
Adjusted r^2..............: 0.132185
Sample size of AE DB......: 2423
Sample size of model......: 336
Missing data %............: 86.13289
- processing TNFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.3594 -0.2351 -0.9075 -0.8277 -1.8130
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0393 -0.6186 -0.1164 0.5245 2.9968
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.132089 0.466571 0.283 0.777292
currentDF[, TRAIT] 0.010954 0.060391 0.181 0.856195
Age 0.001899 0.006432 0.295 0.767958
Gendermale 0.137976 0.119161 1.158 0.247836
ORdate_year2003 -0.225484 0.179670 -1.255 0.210465
ORdate_year2004 -0.903785 0.173517 -5.209 3.56e-07 ***
ORdate_year2005 -0.828797 0.220543 -3.758 0.000206 ***
ORdate_year2006 -1.865749 0.970695 -1.922 0.055550 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9573 on 298 degrees of freedom
Multiple R-squared: 0.1424, Adjusted R-squared: 0.1223
F-statistic: 7.07 on 7 and 298 DF, p-value: 8.128e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: TNFA_rank
Effect size...............: 0.010954
Standard error............: 0.060391
Odds ratio (effect size)..: 1.011
Lower 95% CI..............: 0.898
Upper 95% CI..............: 1.138
T-value...................: 0.181377
P-value...................: 0.8561947
R^2.......................: 0.142418
Adjusted r^2..............: 0.122274
Sample size of AE DB......: 2423
Sample size of model......: 306
Missing data %............: 87.37103
- processing MIF_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.3931 -0.2888 -0.9182 -1.0642 -0.6826
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9759 -0.6988 -0.1331 0.5678 2.9702
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.619940 0.419388 1.478 0.1401
currentDF[, TRAIT] 0.009013 0.055925 0.161 0.8720
Age -0.004610 0.005676 -0.812 0.4171
Gendermale 0.121880 0.105805 1.152 0.2500
ORdate_year2003 -0.301294 0.176000 -1.712 0.0876 .
ORdate_year2004 -0.916586 0.175452 -5.224 2.74e-07 ***
ORdate_year2005 -1.052734 0.179771 -5.856 9.47e-09 ***
ORdate_year2006 -0.673339 0.282532 -2.383 0.0176 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.02 on 427 degrees of freedom
Multiple R-squared: 0.1308, Adjusted R-squared: 0.1166
F-statistic: 9.18 on 7 and 427 DF, p-value: 1.371e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MIF_rank
Effect size...............: 0.009013
Standard error............: 0.055925
Odds ratio (effect size)..: 1.009
Lower 95% CI..............: 0.904
Upper 95% CI..............: 1.126
T-value...................: 0.161168
P-value...................: 0.8720375
R^2.......................: 0.130812
Adjusted r^2..............: 0.116563
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing MCP1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.3023 0.2097 -0.2563 -0.8013 -0.9411 -0.5196
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0954 -0.6862 -0.1195 0.5767 3.0085
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.471837 0.413506 1.141 0.2545
currentDF[, TRAIT] 0.203520 0.049243 4.133 4.32e-05 ***
Age -0.003341 0.005637 -0.593 0.5538
Gendermale 0.090893 0.104877 0.867 0.3866
ORdate_year2003 -0.267411 0.173602 -1.540 0.1242
ORdate_year2004 -0.809982 0.166899 -4.853 1.71e-06 ***
ORdate_year2005 -0.941670 0.170880 -5.511 6.22e-08 ***
ORdate_year2006 -0.525289 0.273184 -1.923 0.0552 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1 on 423 degrees of freedom
Multiple R-squared: 0.1627, Adjusted R-squared: 0.1488
F-statistic: 11.74 on 7 and 423 DF, p-value: 1.111e-13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MCP1_rank
Effect size...............: 0.20352
Standard error............: 0.049243
Odds ratio (effect size)..: 1.226
Lower 95% CI..............: 1.113
Upper 95% CI..............: 1.35
T-value...................: 4.132935
P-value...................: 4.319487e-05
R^2.......................: 0.162694
Adjusted r^2..............: 0.148837
Sample size of AE DB......: 2423
Sample size of model......: 431
Missing data %............: 82.21213
- processing MIP1a_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.3992 0.1664 -0.2543 -0.9918 -1.0730 -0.6045
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0861 -0.7111 -0.1538 0.5743 2.9969
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.684263 0.444221 1.540 0.1243
currentDF[, TRAIT] 0.157843 0.054205 2.912 0.0038 **
Age -0.005388 0.006119 -0.881 0.3791
Gendermale 0.121865 0.113622 1.073 0.2841
ORdate_year2003 -0.271864 0.185532 -1.465 0.1436
ORdate_year2004 -1.000532 0.173699 -5.760 1.72e-08 ***
ORdate_year2005 -1.067814 0.174729 -6.111 2.42e-09 ***
ORdate_year2006 -0.606799 0.280675 -2.162 0.0312 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.034 on 386 degrees of freedom
Multiple R-squared: 0.1546, Adjusted R-squared: 0.1393
F-statistic: 10.09 on 7 and 386 DF, p-value: 1.338e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.157843
Standard error............: 0.054205
Odds ratio (effect size)..: 1.171
Lower 95% CI..............: 1.053
Upper 95% CI..............: 1.302
T-value...................: 2.911974
P-value...................: 0.003800085
R^2.......................: 0.154627
Adjusted r^2..............: 0.139296
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing RANTES_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.3931 -0.2962 -0.9115 -1.0642 -0.6826
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9644 -0.7037 -0.1306 0.5816 2.9877
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.618366 0.432466 1.430 0.1535
currentDF[, TRAIT] 0.001758 0.056046 0.031 0.9750
Age -0.004528 0.005783 -0.783 0.4341
Gendermale 0.121765 0.107805 1.129 0.2593
ORdate_year2003 -0.309602 0.183784 -1.685 0.0928 .
ORdate_year2004 -0.916235 0.186148 -4.922 1.23e-06 ***
ORdate_year2005 -1.059197 0.186301 -5.685 2.45e-08 ***
ORdate_year2006 -0.681405 0.285456 -2.387 0.0174 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.025 on 419 degrees of freedom
Multiple R-squared: 0.128, Adjusted R-squared: 0.1135
F-statistic: 8.789 on 7 and 419 DF, p-value: 4.252e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: RANTES_rank
Effect size...............: 0.001758
Standard error............: 0.056046
Odds ratio (effect size)..: 1.002
Lower 95% CI..............: 0.898
Upper 95% CI..............: 1.118
T-value...................: 0.031367
P-value...................: 0.974992
R^2.......................: 0.128036
Adjusted r^2..............: 0.113469
Sample size of AE DB......: 2423
Sample size of model......: 427
Missing data %............: 82.37722
- processing MIG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.4532 0.1270 -0.2950 -1.0048 -1.1459 -0.7489
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9884 -0.7095 -0.1351 0.5597 2.9038
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.638188 0.423505 1.507 0.13259
currentDF[, TRAIT] 0.119014 0.053602 2.220 0.02694 *
Age -0.003763 0.005845 -0.644 0.52006
Gendermale 0.101203 0.107367 0.943 0.34644
ORdate_year2003 -0.303820 0.177644 -1.710 0.08796 .
ORdate_year2004 -1.005159 0.172890 -5.814 1.22e-08 ***
ORdate_year2005 -1.137169 0.176158 -6.455 3.01e-10 ***
ORdate_year2006 -0.745611 0.277790 -2.684 0.00756 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.021 on 416 degrees of freedom
Multiple R-squared: 0.1393, Adjusted R-squared: 0.1249
F-statistic: 9.622 on 7 and 416 DF, p-value: 4.162e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MIG_rank
Effect size...............: 0.119014
Standard error............: 0.053602
Odds ratio (effect size)..: 1.126
Lower 95% CI..............: 1.014
Upper 95% CI..............: 1.251
T-value...................: 2.22032
P-value...................: 0.02693525
R^2.......................: 0.139348
Adjusted r^2..............: 0.124866
Sample size of AE DB......: 2423
Sample size of model......: 424
Missing data %............: 82.50103
- processing IP10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.4021 0.1745 -0.3183 -0.9780 -1.0638 -0.7078
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8665 -0.6917 -0.1355 0.5949 2.9744
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.451725 0.440187 1.026 0.305445
currentDF[, TRAIT] 0.174173 0.052434 3.322 0.000981 ***
Age -0.001962 0.006085 -0.322 0.747306
Gendermale 0.126639 0.110972 1.141 0.254512
ORdate_year2003 -0.324274 0.182611 -1.776 0.076572 .
ORdate_year2004 -0.985870 0.170107 -5.796 1.43e-08 ***
ORdate_year2005 -1.060500 0.172497 -6.148 1.99e-09 ***
ORdate_year2006 -0.715267 0.286997 -2.492 0.013119 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.015 on 380 degrees of freedom
Multiple R-squared: 0.1582, Adjusted R-squared: 0.1427
F-statistic: 10.2 on 7 and 380 DF, p-value: 1.013e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IP10_rank
Effect size...............: 0.174173
Standard error............: 0.052434
Odds ratio (effect size)..: 1.19
Lower 95% CI..............: 1.074
Upper 95% CI..............: 1.319
T-value...................: 3.321736
P-value...................: 0.0009813086
R^2.......................: 0.158178
Adjusted r^2..............: 0.142671
Sample size of AE DB......: 2423
Sample size of model......: 388
Missing data %............: 83.98679
- processing Eotaxin1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.4255 0.1124 -0.3005 -0.9780 -1.1039 -0.6731
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0451 -0.6895 -0.1583 0.5743 2.9151
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.604346 0.413458 1.462 0.1446
currentDF[, TRAIT] 0.104101 0.052426 1.986 0.0477 *
Age -0.003633 0.005661 -0.642 0.5214
Gendermale 0.099097 0.105903 0.936 0.3499
ORdate_year2003 -0.309333 0.175244 -1.765 0.0783 .
ORdate_year2004 -0.978929 0.169238 -5.784 1.41e-08 ***
ORdate_year2005 -1.098711 0.171978 -6.389 4.38e-10 ***
ORdate_year2006 -0.674763 0.274565 -2.458 0.0144 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.015 on 427 degrees of freedom
Multiple R-squared: 0.1387, Adjusted R-squared: 0.1246
F-statistic: 9.824 on 7 and 427 DF, p-value: 2.248e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.104101
Standard error............: 0.052426
Odds ratio (effect size)..: 1.11
Lower 95% CI..............: 1.001
Upper 95% CI..............: 1.23
T-value...................: 1.98567
P-value...................: 0.04770914
R^2.......................: 0.138713
Adjusted r^2..............: 0.124593
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing TARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.4434 0.1125 -0.3759 -0.9650 -1.0886 -0.7260
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9691 -0.7144 -0.1857 0.5576 2.9038
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.822755 0.495239 1.661 0.097506 .
currentDF[, TRAIT] 0.104841 0.057702 1.817 0.070045 .
Age -0.006156 0.006024 -1.022 0.307478
Gendermale 0.101148 0.113553 0.891 0.373647
ORdate_year2003 -0.420957 0.275237 -1.529 0.127022
ORdate_year2004 -1.000416 0.271164 -3.689 0.000259 ***
ORdate_year2005 -1.112617 0.273565 -4.067 5.84e-05 ***
ORdate_year2006 -0.747691 0.346013 -2.161 0.031355 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.014 on 365 degrees of freedom
Multiple R-squared: 0.1245, Adjusted R-squared: 0.1077
F-statistic: 7.413 on 7 and 365 DF, p-value: 2.431e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: TARC_rank
Effect size...............: 0.104841
Standard error............: 0.057702
Odds ratio (effect size)..: 1.111
Lower 95% CI..............: 0.992
Upper 95% CI..............: 1.244
T-value...................: 1.816946
P-value...................: 0.07004537
R^2.......................: 0.124474
Adjusted r^2..............: 0.107683
Sample size of AE DB......: 2423
Sample size of model......: 373
Missing data %............: 84.60586
- processing PARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.33884 0.07679 -0.27063 -0.83679 -1.00167 -0.62190
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8829 -0.6907 -0.1327 0.5772 2.9704
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.557344 0.417386 1.335 0.1825
currentDF[, TRAIT] 0.076617 0.054541 1.405 0.1608
Age -0.004432 0.005652 -0.784 0.4334
Gendermale 0.125069 0.105499 1.185 0.2365
ORdate_year2003 -0.282512 0.176096 -1.604 0.1094
ORdate_year2004 -0.843751 0.177089 -4.765 2.6e-06 ***
ORdate_year2005 -0.999101 0.176965 -5.646 3.0e-08 ***
ORdate_year2006 -0.623792 0.278395 -2.241 0.0256 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.018 on 427 degrees of freedom
Multiple R-squared: 0.1348, Adjusted R-squared: 0.1206
F-statistic: 9.501 on 7 and 427 DF, p-value: 5.575e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: PARC_rank
Effect size...............: 0.076617
Standard error............: 0.054541
Odds ratio (effect size)..: 1.08
Lower 95% CI..............: 0.97
Upper 95% CI..............: 1.201
T-value...................: 1.404764
P-value...................: 0.1608186
R^2.......................: 0.134758
Adjusted r^2..............: 0.120574
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing MDC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.3931 -0.2293 -0.9469 -1.0683 -0.6826
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0370 -0.7362 -0.1633 0.5877 2.8937
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.741190 0.452364 1.638 0.1021
currentDF[, TRAIT] 0.039660 0.057027 0.695 0.4872
Age -0.006967 0.006140 -1.135 0.2573
Gendermale 0.143839 0.114317 1.258 0.2091
ORdate_year2003 -0.230319 0.191192 -1.205 0.2291
ORdate_year2004 -0.925684 0.181146 -5.110 5.09e-07 ***
ORdate_year2005 -1.021738 0.185452 -5.509 6.62e-08 ***
ORdate_year2006 -0.627823 0.290879 -2.158 0.0315 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.043 on 383 degrees of freedom
Multiple R-squared: 0.1429, Adjusted R-squared: 0.1272
F-statistic: 9.122 on 7 and 383 DF, p-value: 1.954e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MDC_rank
Effect size...............: 0.03966
Standard error............: 0.057027
Odds ratio (effect size)..: 1.04
Lower 95% CI..............: 0.93
Upper 95% CI..............: 1.164
T-value...................: 0.695461
P-value...................: 0.4871882
R^2.......................: 0.1429
Adjusted r^2..............: 0.127235
Sample size of AE DB......: 2423
Sample size of model......: 391
Missing data %............: 83.86298
- processing OPG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.3639 0.1589 -0.2589 -0.8567 -1.0585 -0.7001
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8537 -0.7062 -0.1363 0.5453 3.0342
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.511244 0.413761 1.236 0.21729
currentDF[, TRAIT] 0.153174 0.049160 3.116 0.00196 **
Age -0.003048 0.005653 -0.539 0.59007
Gendermale 0.091928 0.105333 0.873 0.38330
ORdate_year2003 -0.269185 0.174827 -1.540 0.12437
ORdate_year2004 -0.863862 0.167247 -5.165 3.70e-07 ***
ORdate_year2005 -1.056853 0.169998 -6.217 1.21e-09 ***
ORdate_year2006 -0.701422 0.273066 -2.569 0.01055 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.01 on 426 degrees of freedom
Multiple R-squared: 0.1503, Adjusted R-squared: 0.1363
F-statistic: 10.76 on 7 and 426 DF, p-value: 1.645e-12
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: OPG_rank
Effect size...............: 0.153174
Standard error............: 0.04916
Odds ratio (effect size)..: 1.166
Lower 95% CI..............: 1.058
Upper 95% CI..............: 1.283
T-value...................: 3.115799
P-value...................: 0.001958534
R^2.......................: 0.150269
Adjusted r^2..............: 0.136306
Sample size of AE DB......: 2423
Sample size of model......: 434
Missing data %............: 82.08832
- processing sICAM1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.36505 0.07498 -0.29002 -0.87633 -1.02730 -0.61650
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8946 -0.6947 -0.1261 0.5791 2.9167
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.533038 0.419728 1.270 0.2048
currentDF[, TRAIT] 0.071425 0.050306 1.420 0.1564
Age -0.003655 0.005694 -0.642 0.5213
Gendermale 0.123489 0.105481 1.171 0.2424
ORdate_year2003 -0.299992 0.175590 -1.708 0.0883 .
ORdate_year2004 -0.884945 0.169749 -5.213 2.90e-07 ***
ORdate_year2005 -1.026950 0.173027 -5.935 6.08e-09 ***
ORdate_year2006 -0.623714 0.278322 -2.241 0.0255 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.017 on 427 degrees of freedom
Multiple R-squared: 0.1348, Adjusted R-squared: 0.1207
F-statistic: 9.508 on 7 and 427 DF, p-value: 5.467e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: sICAM1_rank
Effect size...............: 0.071425
Standard error............: 0.050306
Odds ratio (effect size)..: 1.074
Lower 95% CI..............: 0.973
Upper 95% CI..............: 1.185
T-value...................: 1.419807
P-value...................: 0.1563935
R^2.......................: 0.134844
Adjusted r^2..............: 0.120661
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing VEGFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.5513 0.1602 -0.4503 -1.0052 -1.4770 -0.9309
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8412 -0.6755 -0.2304 0.5087 3.1906
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.587732 0.439848 1.336 0.18232
currentDF[, TRAIT] 0.156148 0.057632 2.709 0.00706 **
Age -0.002266 0.005865 -0.386 0.69946
Gendermale 0.151114 0.112151 1.347 0.17869
ORdate_year2003 -0.437098 0.200672 -2.178 0.03004 *
ORdate_year2004 -1.000781 0.186700 -5.360 1.49e-07 ***
ORdate_year2005 -1.456274 0.210706 -6.911 2.19e-11 ***
ORdate_year2006 -0.926595 0.300195 -3.087 0.00218 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9735 on 360 degrees of freedom
Multiple R-squared: 0.1746, Adjusted R-squared: 0.1586
F-statistic: 10.88 on 7 and 360 DF, p-value: 1.813e-12
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: VEGFA_rank
Effect size...............: 0.156148
Standard error............: 0.057632
Odds ratio (effect size)..: 1.169
Lower 95% CI..............: 1.044
Upper 95% CI..............: 1.309
T-value...................: 2.709377
P-value...................: 0.007063241
R^2.......................: 0.174621
Adjusted r^2..............: 0.158572
Sample size of AE DB......: 2423
Sample size of model......: 368
Missing data %............: 84.81222
- processing TGFB_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.4249 -0.3192 -0.9353 -1.1060 -0.5643
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9623 -0.7267 -0.1376 0.5864 2.9536
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.656362 0.422190 1.555 0.1208
currentDF[, TRAIT] 0.036041 0.052078 0.692 0.4893
Age -0.004886 0.005761 -0.848 0.3968
Gendermale 0.119587 0.108656 1.101 0.2717
ORdate_year2003 -0.320732 0.177135 -1.811 0.0709 .
ORdate_year2004 -0.910521 0.173560 -5.246 2.48e-07 ***
ORdate_year2005 -1.077192 0.174980 -6.156 1.75e-09 ***
ORdate_year2006 -0.536254 0.291278 -1.841 0.0663 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.029 on 417 degrees of freedom
Multiple R-squared: 0.1366, Adjusted R-squared: 0.1221
F-statistic: 9.423 on 7 and 417 DF, p-value: 7.229e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: TGFB_rank
Effect size...............: 0.036041
Standard error............: 0.052078
Odds ratio (effect size)..: 1.037
Lower 95% CI..............: 0.936
Upper 95% CI..............: 1.148
T-value...................: 0.692049
P-value...................: 0.4892915
R^2.......................: 0.136579
Adjusted r^2..............: 0.122086
Sample size of AE DB......: 2423
Sample size of model......: 425
Missing data %............: 82.45976
- processing MMP2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.2534 0.1092 0.2000 -0.3162 -0.9078 -1.1055 -0.8481
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0064 -0.6931 -0.1507 0.5357 3.1382
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.475161 0.417983 1.137 0.2563
currentDF[, TRAIT] 0.106666 0.052051 2.049 0.0411 *
Age -0.003269 0.005699 -0.574 0.5665
Gendermale 0.199617 0.107521 1.857 0.0641 .
ORdate_year2003 -0.322593 0.171398 -1.882 0.0605 .
ORdate_year2004 -0.908166 0.165290 -5.494 6.79e-08 ***
ORdate_year2005 -1.102677 0.169229 -6.516 2.06e-10 ***
ORdate_year2006 -0.837004 0.531779 -1.574 0.1162
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.023 on 422 degrees of freedom
Multiple R-squared: 0.1542, Adjusted R-squared: 0.1401
F-statistic: 10.99 on 7 and 422 DF, p-value: 8.983e-13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MMP2_rank
Effect size...............: 0.106666
Standard error............: 0.052051
Odds ratio (effect size)..: 1.113
Lower 95% CI..............: 1.005
Upper 95% CI..............: 1.232
T-value...................: 2.049243
P-value...................: 0.04105619
R^2.......................: 0.154167
Adjusted r^2..............: 0.140136
Sample size of AE DB......: 2423
Sample size of model......: 430
Missing data %............: 82.25341
- processing MMP8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.4005 0.1541 -0.3038 -0.9148 -1.1389 -0.6780
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0611 -0.7314 -0.1614 0.5774 2.9909
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.599550 0.413214 1.451 0.14754
currentDF[, TRAIT] 0.143826 0.051470 2.794 0.00544 **
Age -0.004160 0.005654 -0.736 0.46226
Gendermale 0.122007 0.108020 1.129 0.25933
ORdate_year2003 -0.310900 0.170703 -1.821 0.06927 .
ORdate_year2004 -0.919844 0.163996 -5.609 3.69e-08 ***
ORdate_year2005 -1.133524 0.168093 -6.743 5.11e-11 ***
ORdate_year2006 -0.710539 0.531340 -1.337 0.18186
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.018 on 422 degrees of freedom
Multiple R-squared: 0.1613, Adjusted R-squared: 0.1474
F-statistic: 11.59 on 7 and 422 DF, p-value: 1.691e-13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MMP8_rank
Effect size...............: 0.143826
Standard error............: 0.05147
Odds ratio (effect size)..: 1.155
Lower 95% CI..............: 1.044
Upper 95% CI..............: 1.277
T-value...................: 2.794391
P-value...................: 0.005436768
R^2.......................: 0.161269
Adjusted r^2..............: 0.147357
Sample size of AE DB......: 2423
Sample size of model......: 430
Missing data %............: 82.25341
- processing MMP9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.28157 0.08543 0.15683 -0.30727 -0.91114 -1.10824 -0.78312
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9882 -0.7217 -0.1499 0.5582 2.9809
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.592380 0.415557 1.426 0.1547
currentDF[, TRAIT] 0.086866 0.050255 1.729 0.0846 .
Age -0.004603 0.005690 -0.809 0.4189
Gendermale 0.156869 0.107460 1.460 0.1451
ORdate_year2003 -0.316330 0.171650 -1.843 0.0660 .
ORdate_year2004 -0.910102 0.165712 -5.492 6.88e-08 ***
ORdate_year2005 -1.102938 0.169634 -6.502 2.24e-10 ***
ORdate_year2006 -0.766522 0.533909 -1.436 0.1518
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.024 on 422 degrees of freedom
Multiple R-squared: 0.1518, Adjusted R-squared: 0.1377
F-statistic: 10.79 on 7 and 422 DF, p-value: 1.577e-12
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MMP9_rank
Effect size...............: 0.086866
Standard error............: 0.050255
Odds ratio (effect size)..: 1.091
Lower 95% CI..............: 0.988
Upper 95% CI..............: 1.204
T-value...................: 1.728501
P-value...................: 0.0846298
R^2.......................: 0.151755
Adjusted r^2..............: 0.137685
Sample size of AE DB......: 2423
Sample size of model......: 430
Missing data %............: 82.25341
Analysis of MCP1_pg_ml_2015_rank.
- processing IL2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.01916 -0.10092 0.19380 -0.34796 -0.84807 -0.49894 1.34500
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8944 -0.5425 -0.1061 0.5233 2.6690
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.316819 0.394761 -0.803 0.42280
currentDF[, TRAIT] -0.097552 0.049498 -1.971 0.04957 *
Age 0.004395 0.005456 0.806 0.42102
Gendermale 0.191557 0.101755 1.883 0.06063 .
ORdate_year2003 -0.337074 0.149658 -2.252 0.02495 *
ORdate_year2004 -0.846591 0.143102 -5.916 8.19e-09 ***
ORdate_year2005 -0.506798 0.170505 -2.972 0.00317 **
ORdate_year2006 1.313991 0.881081 1.491 0.13682
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8617 on 333 degrees of freedom
Multiple R-squared: 0.1366, Adjusted R-squared: 0.1185
F-statistic: 7.529 on 7 and 333 DF, p-value: 1.992e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL2_rank
Effect size...............: -0.097552
Standard error............: 0.049498
Odds ratio (effect size)..: 0.907
Lower 95% CI..............: 0.823
Upper 95% CI..............: 0.999
T-value...................: -1.970818
P-value...................: 0.04957258
R^2.......................: 0.136642
Adjusted r^2..............: 0.118493
Sample size of AE DB......: 2423
Sample size of model......: 341
Missing data %............: 85.92654
- processing IL4_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005
0.01613 -0.13592 0.17675 -0.34599 -0.89623 -0.43581
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8079 -0.5423 -0.1172 0.5377 2.5734
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.392461 0.402691 -0.975 0.3305
currentDF[, TRAIT] -0.129645 0.050342 -2.575 0.0105 *
Age 0.006001 0.005528 1.085 0.2786
Gendermale 0.172421 0.105035 1.642 0.1017
ORdate_year2003 -0.328210 0.149896 -2.190 0.0293 *
ORdate_year2004 -0.891522 0.143187 -6.226 1.56e-09 ***
ORdate_year2005 -0.443816 0.187019 -2.373 0.0183 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8396 on 309 degrees of freedom
Multiple R-squared: 0.1553, Adjusted R-squared: 0.1389
F-statistic: 9.468 on 6 and 309 DF, p-value: 1.491e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL4_rank
Effect size...............: -0.129645
Standard error............: 0.050342
Odds ratio (effect size)..: 0.878
Lower 95% CI..............: 0.796
Upper 95% CI..............: 0.97
T-value...................: -2.575261
P-value...................: 0.01048127
R^2.......................: 0.1553
Adjusted r^2..............: 0.138898
Sample size of AE DB......: 2423
Sample size of model......: 316
Missing data %............: 86.95832
- processing IL5_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005
0.006071 -0.101897 0.238759 -0.400063 -0.892123 -0.538381
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.85348 -0.56530 -0.07814 0.50975 2.53264
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.386432 0.386250 -1.000 0.31781
currentDF[, TRAIT] -0.098261 0.048835 -2.012 0.04502 *
Age 0.005850 0.005374 1.089 0.27716
Gendermale 0.233265 0.100647 2.318 0.02108 *
ORdate_year2003 -0.385982 0.148021 -2.608 0.00953 **
ORdate_year2004 -0.892139 0.142065 -6.280 1.06e-09 ***
ORdate_year2005 -0.553573 0.174760 -3.168 0.00168 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8483 on 331 degrees of freedom
Multiple R-squared: 0.1369, Adjusted R-squared: 0.1213
F-statistic: 8.754 on 6 and 331 DF, p-value: 7.324e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL5_rank
Effect size...............: -0.098261
Standard error............: 0.048835
Odds ratio (effect size)..: 0.906
Lower 95% CI..............: 0.824
Upper 95% CI..............: 0.997
T-value...................: -2.012114
P-value...................: 0.04501779
R^2.......................: 0.136945
Adjusted r^2..............: 0.1213
Sample size of AE DB......: 2423
Sample size of model......: 338
Missing data %............: 86.05035
- processing IL6_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.07805 0.30482 -0.33653 -0.83468 -0.80098 0.24667
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.88112 -0.58819 -0.06939 0.55371 2.81174
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.576443 0.407076 -1.416 0.15766
currentDF[, TRAIT] 0.050379 0.051367 0.981 0.32740
Age 0.007087 0.005649 1.254 0.21053
Gendermale 0.310943 0.104441 2.977 0.00312 **
ORdate_year2003 -0.298100 0.154822 -1.925 0.05500 .
ORdate_year2004 -0.809866 0.147740 -5.482 8.16e-08 ***
ORdate_year2005 -0.832447 0.170191 -4.891 1.54e-06 ***
ORdate_year2006 0.228490 0.649653 0.352 0.72527
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8959 on 344 degrees of freedom
Multiple R-squared: 0.1387, Adjusted R-squared: 0.1212
F-statistic: 7.915 on 7 and 344 DF, p-value: 6.574e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL6_rank
Effect size...............: 0.050379
Standard error............: 0.051367
Odds ratio (effect size)..: 1.052
Lower 95% CI..............: 0.951
Upper 95% CI..............: 1.163
T-value...................: 0.980766
P-value...................: 0.3273973
R^2.......................: 0.138718
Adjusted r^2..............: 0.121192
Sample size of AE DB......: 2423
Sample size of model......: 352
Missing data %............: 85.47255
- processing IL8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.06486 0.26331 0.30842 -0.42673 -0.94757 -1.07264 -0.55165
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.05503 -0.53989 -0.05623 0.47611 2.79718
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.164896 0.387149 -0.426 0.67044
currentDF[, TRAIT] 0.260941 0.048013 5.435 1.06e-07 ***
Age 0.003408 0.005361 0.636 0.52547
Gendermale 0.310039 0.101638 3.050 0.00247 **
ORdate_year2003 -0.423624 0.149836 -2.827 0.00498 **
ORdate_year2004 -0.949713 0.142607 -6.660 1.13e-10 ***
ORdate_year2005 -1.081420 0.160236 -6.749 6.60e-11 ***
ORdate_year2006 -0.565883 0.442943 -1.278 0.20229
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8491 on 334 degrees of freedom
Multiple R-squared: 0.2179, Adjusted R-squared: 0.2015
F-statistic: 13.29 on 7 and 334 DF, p-value: 3.895e-15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL8_rank
Effect size...............: 0.260941
Standard error............: 0.048013
Odds ratio (effect size)..: 1.298
Lower 95% CI..............: 1.182
Upper 95% CI..............: 1.426
T-value...................: 5.434817
P-value...................: 1.059904e-07
R^2.......................: 0.217897
Adjusted r^2..............: 0.201505
Sample size of AE DB......: 2423
Sample size of model......: 342
Missing data %............: 85.88527
- processing IL9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.08609 0.10289 0.33342 -0.29430 -0.89329 -0.78279 -0.25877
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.75582 -0.59818 -0.09229 0.56429 2.84771
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.184564 0.400144 -0.461 0.64489
currentDF[, TRAIT] 0.104433 0.048564 2.150 0.03216 *
Age 0.001455 0.005505 0.264 0.79174
Gendermale 0.332829 0.101742 3.271 0.00117 **
ORdate_year2003 -0.290464 0.168098 -1.728 0.08483 .
ORdate_year2004 -0.892760 0.153967 -5.798 1.43e-08 ***
ORdate_year2005 -0.783805 0.154636 -5.069 6.32e-07 ***
ORdate_year2006 -0.262771 0.248096 -1.059 0.29022
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9143 on 373 degrees of freedom
Multiple R-squared: 0.1432, Adjusted R-squared: 0.1271
F-statistic: 8.903 on 7 and 373 DF, p-value: 3.764e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL9_rank
Effect size...............: 0.104433
Standard error............: 0.048564
Odds ratio (effect size)..: 1.11
Lower 95% CI..............: 1.009
Upper 95% CI..............: 1.221
T-value...................: 2.150429
P-value...................: 0.03216348
R^2.......................: 0.143158
Adjusted r^2..............: 0.127078
Sample size of AE DB......: 2423
Sample size of model......: 381
Missing data %............: 84.27569
- processing IL10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005
-0.12284 -0.08206 0.27422 -0.28904 -0.82571 -0.26386
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7810 -0.5712 -0.0882 0.5519 2.5556
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.598671 0.410659 -1.458 0.1460
currentDF[, TRAIT] -0.076811 0.053821 -1.427 0.1546
Age 0.007053 0.005703 1.237 0.2172
Gendermale 0.266784 0.108543 2.458 0.0146 *
ORdate_year2003 -0.268543 0.153226 -1.753 0.0807 .
ORdate_year2004 -0.823700 0.146979 -5.604 4.81e-08 ***
ORdate_year2005 -0.261847 0.213240 -1.228 0.2204
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8553 on 294 degrees of freedom
Multiple R-squared: 0.1426, Adjusted R-squared: 0.1251
F-statistic: 8.146 on 6 and 294 DF, p-value: 3.69e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL10_rank
Effect size...............: -0.076811
Standard error............: 0.053821
Odds ratio (effect size)..: 0.926
Lower 95% CI..............: 0.833
Upper 95% CI..............: 1.029
T-value...................: -1.427144
P-value...................: 0.1545996
R^2.......................: 0.142553
Adjusted r^2..............: 0.125055
Sample size of AE DB......: 2423
Sample size of model......: 301
Missing data %............: 87.57738
- processing IL12_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005
-0.10766 -0.08868 0.24796 -0.29420 -0.79349 -0.50888
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7949 -0.5760 -0.1074 0.5575 2.5884
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.612340 0.408817 -1.498 0.13520
currentDF[, TRAIT] -0.079996 0.051496 -1.553 0.12134
Age 0.007447 0.005657 1.317 0.18896
Gendermale 0.236517 0.105153 2.249 0.02520 *
ORdate_year2003 -0.267256 0.154929 -1.725 0.08552 .
ORdate_year2004 -0.786264 0.145225 -5.414 1.24e-07 ***
ORdate_year2005 -0.515959 0.194028 -2.659 0.00824 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8524 on 309 degrees of freedom
Multiple R-squared: 0.1271, Adjusted R-squared: 0.1102
F-statistic: 7.499 on 6 and 309 DF, p-value: 1.624e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL12_rank
Effect size...............: -0.079996
Standard error............: 0.051496
Odds ratio (effect size)..: 0.923
Lower 95% CI..............: 0.834
Upper 95% CI..............: 1.021
T-value...................: -1.553427
P-value...................: 0.1213447
R^2.......................: 0.127102
Adjusted r^2..............: 0.110152
Sample size of AE DB......: 2423
Sample size of model......: 316
Missing data %............: 86.95832
- processing IL13_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.01507 0.11818 0.25714 -0.40098 -0.90902 -0.85999 -0.28985
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.77521 -0.61731 -0.09504 0.59972 2.78243
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.259398 0.365200 -0.710 0.47791
currentDF[, TRAIT] 0.122499 0.045038 2.720 0.00680 **
Age 0.004092 0.005017 0.816 0.41522
Gendermale 0.254539 0.092960 2.738 0.00644 **
ORdate_year2003 -0.392793 0.155607 -2.524 0.01196 *
ORdate_year2004 -0.910806 0.148751 -6.123 2.09e-09 ***
ORdate_year2005 -0.865178 0.151775 -5.700 2.24e-08 ***
ORdate_year2006 -0.302719 0.241490 -1.254 0.21069
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.893 on 426 degrees of freedom
Multiple R-squared: 0.1343, Adjusted R-squared: 0.12
F-statistic: 9.438 on 7 and 426 DF, p-value: 6.664e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL13_rank
Effect size...............: 0.122499
Standard error............: 0.045038
Odds ratio (effect size)..: 1.13
Lower 95% CI..............: 1.035
Upper 95% CI..............: 1.235
T-value...................: 2.719915
P-value...................: 0.006797
R^2.......................: 0.134268
Adjusted r^2..............: 0.120043
Sample size of AE DB......: 2423
Sample size of model......: 434
Missing data %............: 82.08832
- processing IL21_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.01032 0.10830 0.25129 -0.38921 -0.89992 -0.84548 -0.27257
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.75574 -0.61898 -0.09806 0.59333 2.79926
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.253364 0.364079 -0.696 0.48687
currentDF[, TRAIT] 0.112270 0.045496 2.468 0.01399 *
Age 0.003924 0.004991 0.786 0.43214
Gendermale 0.249112 0.092909 2.681 0.00762 **
ORdate_year2003 -0.381763 0.155120 -2.461 0.01425 *
ORdate_year2004 -0.901427 0.148592 -6.066 2.88e-09 ***
ORdate_year2005 -0.850012 0.151333 -5.617 3.51e-08 ***
ORdate_year2006 -0.284344 0.241699 -1.176 0.24008
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8933 on 427 degrees of freedom
Multiple R-squared: 0.1317, Adjusted R-squared: 0.1175
F-statistic: 9.252 on 7 and 427 DF, p-value: 1.12e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL21_rank
Effect size...............: 0.11227
Standard error............: 0.045496
Odds ratio (effect size)..: 1.119
Lower 95% CI..............: 1.023
Upper 95% CI..............: 1.223
T-value...................: 2.467668
P-value...................: 0.0139908
R^2.......................: 0.1317
Adjusted r^2..............: 0.117465
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing INFG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.1420 0.3196 -0.2797 -0.8123 -0.6402 -1.1206
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.75957 -0.58355 -0.08904 0.55190 2.58926
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.563267 0.405022 -1.391 0.16526
currentDF[, TRAIT] -0.064347 0.055069 -1.168 0.24346
Age 0.006990 0.005584 1.252 0.21155
Gendermale 0.299864 0.105579 2.840 0.00479 **
ORdate_year2003 -0.302088 0.152861 -1.976 0.04897 *
ORdate_year2004 -0.852733 0.146347 -5.827 1.35e-08 ***
ORdate_year2005 -0.730820 0.185054 -3.949 9.60e-05 ***
ORdate_year2006 -1.246259 0.643656 -1.936 0.05370 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8777 on 328 degrees of freedom
Multiple R-squared: 0.1391, Adjusted R-squared: 0.1207
F-statistic: 7.572 on 7 and 328 DF, p-value: 1.809e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: INFG_rank
Effect size...............: -0.064347
Standard error............: 0.055069
Odds ratio (effect size)..: 0.938
Lower 95% CI..............: 0.842
Upper 95% CI..............: 1.045
T-value...................: -1.168479
P-value...................: 0.2434621
R^2.......................: 0.139118
Adjusted r^2..............: 0.120745
Sample size of AE DB......: 2423
Sample size of model......: 336
Missing data %............: 86.13289
- processing TNFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.07174 -0.10265 0.26219 -0.34108 -0.86794 -0.53184 -1.00966
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.78552 -0.55441 -0.07221 0.57054 2.55868
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.431645 0.411186 -1.050 0.29468
currentDF[, TRAIT] -0.098705 0.053222 -1.855 0.06464 .
Age 0.005318 0.005668 0.938 0.34887
Gendermale 0.256077 0.105016 2.438 0.01533 *
ORdate_year2003 -0.326014 0.158342 -2.059 0.04037 *
ORdate_year2004 -0.863726 0.152920 -5.648 3.78e-08 ***
ORdate_year2005 -0.549417 0.194363 -2.827 0.00502 **
ORdate_year2006 -1.047044 0.855468 -1.224 0.22194
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8437 on 298 degrees of freedom
Multiple R-squared: 0.1397, Adjusted R-squared: 0.1195
F-statistic: 6.914 on 7 and 298 DF, p-value: 1.241e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: TNFA_rank
Effect size...............: -0.098705
Standard error............: 0.053222
Odds ratio (effect size)..: 0.906
Lower 95% CI..............: 0.816
Upper 95% CI..............: 1.006
T-value...................: -1.854583
P-value...................: 0.06464346
R^2.......................: 0.139715
Adjusted r^2..............: 0.119507
Sample size of AE DB......: 2423
Sample size of model......: 306
Missing data %............: 87.37103
- processing MIF_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.03744 0.26848 -0.34531 -0.84789 -0.80713 -0.29478
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.71060 -0.61023 -0.08462 0.56060 2.95198
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.241460 0.369827 -0.653 0.51417
currentDF[, TRAIT] 0.029300 0.049316 0.594 0.55274
Age 0.002759 0.005005 0.551 0.58173
Gendermale 0.265296 0.093302 2.843 0.00468 **
ORdate_year2003 -0.339981 0.155201 -2.191 0.02902 *
ORdate_year2004 -0.820764 0.154718 -5.305 1.81e-07 ***
ORdate_year2005 -0.781311 0.158526 -4.929 1.19e-06 ***
ORdate_year2006 -0.270832 0.249144 -1.087 0.27763
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8993 on 427 degrees of freedom
Multiple R-squared: 0.12, Adjusted R-squared: 0.1056
F-statistic: 8.322 on 7 and 427 DF, p-value: 1.545e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MIF_rank
Effect size...............: 0.0293
Standard error............: 0.049316
Odds ratio (effect size)..: 1.03
Lower 95% CI..............: 0.935
Upper 95% CI..............: 1.134
T-value...................: 0.594133
P-value...................: 0.5527379
R^2.......................: 0.120044
Adjusted r^2..............: 0.105619
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing MCP1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.1101 0.2124 0.2392 -0.3179 -0.7282 -0.6802 -0.1259
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9692 -0.6070 -0.1097 0.5730 2.9425
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.375217 0.359962 -1.042 0.29783
currentDF[, TRAIT] 0.214887 0.042867 5.013 7.90e-07 ***
Age 0.003911 0.004907 0.797 0.42587
Gendermale 0.236910 0.091297 2.595 0.00979 **
ORdate_year2003 -0.307567 0.151123 -2.035 0.04245 *
ORdate_year2004 -0.726382 0.145288 -5.000 8.43e-07 ***
ORdate_year2005 -0.682628 0.148753 -4.589 5.88e-06 ***
ORdate_year2006 -0.136424 0.237811 -0.574 0.56650
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8707 on 423 degrees of freedom
Multiple R-squared: 0.1681, Adjusted R-squared: 0.1544
F-statistic: 12.21 on 7 and 423 DF, p-value: 3.039e-14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MCP1_rank
Effect size...............: 0.214887
Standard error............: 0.042867
Odds ratio (effect size)..: 1.24
Lower 95% CI..............: 1.14
Upper 95% CI..............: 1.348
T-value...................: 5.012879
P-value...................: 7.899727e-07
R^2.......................: 0.168122
Adjusted r^2..............: 0.154356
Sample size of AE DB......: 2423
Sample size of model......: 431
Missing data %............: 82.21213
- processing MIP1a_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.05552 0.12811 0.30292 -0.32223 -0.92106 -0.80221 -0.23918
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7495 -0.6173 -0.1058 0.5453 2.7985
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.1163509 0.3884834 -0.300 0.76472
currentDF[, TRAIT] 0.1288922 0.0474035 2.719 0.00684 **
Age 0.0009019 0.0053509 0.169 0.86624
Gendermale 0.3023529 0.0993658 3.043 0.00250 **
ORdate_year2003 -0.3197343 0.1622527 -1.971 0.04948 *
ORdate_year2004 -0.9209180 0.1519043 -6.062 3.19e-09 ***
ORdate_year2005 -0.8029970 0.1528058 -5.255 2.45e-07 ***
ORdate_year2006 -0.2416727 0.2454586 -0.985 0.32545
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9039 on 386 degrees of freedom
Multiple R-squared: 0.1472, Adjusted R-squared: 0.1317
F-statistic: 9.517 on 7 and 386 DF, p-value: 6.452e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.128892
Standard error............: 0.047403
Odds ratio (effect size)..: 1.138
Lower 95% CI..............: 1.037
Upper 95% CI..............: 1.248
T-value...................: 2.719043
P-value...................: 0.006842628
R^2.......................: 0.147181
Adjusted r^2..............: 0.131716
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing RANTES_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.04069 0.27341 -0.36047 -0.83747 -0.80717 -0.29542
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7089 -0.5897 -0.1111 0.5782 2.9643
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.314649 0.378256 -0.832 0.40597
currentDF[, TRAIT] 0.047091 0.049021 0.961 0.33729
Age 0.003393 0.005058 0.671 0.50267
Gendermale 0.266281 0.094291 2.824 0.00497 **
ORdate_year2003 -0.317473 0.160747 -1.975 0.04893 *
ORdate_year2004 -0.771425 0.162814 -4.738 2.96e-06 ***
ORdate_year2005 -0.750379 0.162948 -4.605 5.48e-06 ***
ORdate_year2006 -0.246746 0.249674 -0.988 0.32359
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8964 on 419 degrees of freedom
Multiple R-squared: 0.1198, Adjusted R-squared: 0.1051
F-statistic: 8.15 on 7 and 419 DF, p-value: 2.578e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: RANTES_rank
Effect size...............: 0.047091
Standard error............: 0.049021
Odds ratio (effect size)..: 1.048
Lower 95% CI..............: 0.952
Upper 95% CI..............: 1.154
T-value...................: 0.960638
P-value...................: 0.3372884
R^2.......................: 0.119834
Adjusted r^2..............: 0.10513
Sample size of AE DB......: 2423
Sample size of model......: 427
Missing data %............: 82.37722
- processing MIG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.0006877 0.0730112 0.2651666 -0.3257853 -0.8934078 -0.8499897 -0.3325036
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7736 -0.5906 -0.1025 0.6068 2.8726
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.204862 0.373265 -0.549 0.58341
currentDF[, TRAIT] 0.077243 0.047243 1.635 0.10281
Age 0.003055 0.005152 0.593 0.55357
Gendermale 0.263306 0.094631 2.782 0.00564 **
ORdate_year2003 -0.319036 0.156570 -2.038 0.04222 *
ORdate_year2004 -0.896032 0.152380 -5.880 8.42e-09 ***
ORdate_year2005 -0.855854 0.155261 -5.512 6.22e-08 ***
ORdate_year2006 -0.344425 0.244836 -1.407 0.16025
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9 on 416 degrees of freedom
Multiple R-squared: 0.126, Adjusted R-squared: 0.1113
F-statistic: 8.567 on 7 and 416 DF, p-value: 8.03e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MIG_rank
Effect size...............: 0.077243
Standard error............: 0.047243
Odds ratio (effect size)..: 1.08
Lower 95% CI..............: 0.985
Upper 95% CI..............: 1.185
T-value...................: 1.634993
P-value...................: 0.102807
R^2.......................: 0.125995
Adjusted r^2..............: 0.111288
Sample size of AE DB......: 2423
Sample size of model......: 424
Missing data %............: 82.50103
- processing IP10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.0537 0.1290 0.3032 -0.3851 -0.9122 -0.8016 -0.3111
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.68383 -0.58012 -0.09862 0.59013 2.82591
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.274118 0.388399 -0.706 0.48077
currentDF[, TRAIT] 0.131912 0.046266 2.851 0.00459 **
Age 0.003274 0.005369 0.610 0.54234
Gendermale 0.300555 0.097916 3.070 0.00230 **
ORdate_year2003 -0.375965 0.161127 -2.333 0.02015 *
ORdate_year2004 -0.911893 0.150094 -6.075 3.00e-09 ***
ORdate_year2005 -0.804256 0.152203 -5.284 2.13e-07 ***
ORdate_year2006 -0.319166 0.253232 -1.260 0.20831
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8956 on 380 degrees of freedom
Multiple R-squared: 0.1449, Adjusted R-squared: 0.1291
F-statistic: 9.196 on 7 and 380 DF, p-value: 1.616e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IP10_rank
Effect size...............: 0.131912
Standard error............: 0.046266
Odds ratio (effect size)..: 1.141
Lower 95% CI..............: 1.042
Upper 95% CI..............: 1.249
T-value...................: 2.85119
P-value...................: 0.004593293
R^2.......................: 0.14486
Adjusted r^2..............: 0.129107
Sample size of AE DB......: 2423
Sample size of model......: 388
Missing data %............: 83.98679
- processing Eotaxin1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.003403 0.094076 0.247654 -0.354907 -0.896856 -0.840188 -0.284120
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7987 -0.6057 -0.1067 0.5916 2.8425
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.233799 0.364539 -0.641 0.52164
currentDF[, TRAIT] 0.097099 0.046223 2.101 0.03626 *
Age 0.003526 0.004991 0.707 0.48025
Gendermale 0.245595 0.093373 2.630 0.00884 **
ORdate_year2003 -0.347073 0.154510 -2.246 0.02520 *
ORdate_year2004 -0.898065 0.149214 -6.019 3.79e-09 ***
ORdate_year2005 -0.844062 0.151630 -5.567 4.60e-08 ***
ORdate_year2006 -0.295086 0.242079 -1.219 0.22353
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8951 on 427 degrees of freedom
Multiple R-squared: 0.1283, Adjusted R-squared: 0.114
F-statistic: 8.98 on 7 and 427 DF, p-value: 2.409e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.097099
Standard error............: 0.046223
Odds ratio (effect size)..: 1.102
Lower 95% CI..............: 1.007
Upper 95% CI..............: 1.206
T-value...................: 2.100645
P-value...................: 0.03625793
R^2.......................: 0.128325
Adjusted r^2..............: 0.114035
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing TARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.05113 0.09222 0.24662 -0.47232 -0.92002 -0.85499 -0.36053
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7554 -0.6037 -0.1083 0.5913 2.7577
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.084478 0.437929 -0.193 0.847141
currentDF[, TRAIT] 0.094493 0.051024 1.852 0.064845 .
Age 0.001939 0.005327 0.364 0.716059
Gendermale 0.245455 0.100412 2.444 0.014979 *
ORdate_year2003 -0.462800 0.243385 -1.902 0.058023 .
ORdate_year2004 -0.914706 0.239784 -3.815 0.000160 ***
ORdate_year2005 -0.851530 0.241907 -3.520 0.000486 ***
ORdate_year2006 -0.361846 0.305971 -1.183 0.237731
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8968 on 365 degrees of freedom
Multiple R-squared: 0.1078, Adjusted R-squared: 0.09069
F-statistic: 6.3 on 7 and 365 DF, p-value: 5.402e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: TARC_rank
Effect size...............: 0.094493
Standard error............: 0.051024
Odds ratio (effect size)..: 1.099
Lower 95% CI..............: 0.995
Upper 95% CI..............: 1.215
T-value...................: 1.851921
P-value...................: 0.06484451
R^2.......................: 0.107796
Adjusted r^2..............: 0.090686
Sample size of AE DB......: 2423
Sample size of model......: 373
Missing data %............: 84.60586
- processing PARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.10244 0.08913 0.27152 -0.32431 -0.75357 -0.73455 -0.22474
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.72442 -0.62142 -0.09571 0.54070 2.99752
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.295066 0.367545 -0.803 0.42253
currentDF[, TRAIT] 0.089963 0.048028 1.873 0.06173 .
Age 0.002839 0.004977 0.570 0.56872
Gendermale 0.270429 0.092901 2.911 0.00379 **
ORdate_year2003 -0.317564 0.155068 -2.048 0.04118 *
ORdate_year2004 -0.752400 0.155942 -4.825 1.95e-06 ***
ORdate_year2005 -0.736133 0.155833 -4.724 3.15e-06 ***
ORdate_year2006 -0.233192 0.245152 -0.951 0.34203
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.896 on 427 degrees of freedom
Multiple R-squared: 0.1265, Adjusted R-squared: 0.1122
F-statistic: 8.834 on 7 and 427 DF, p-value: 3.642e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: PARC_rank
Effect size...............: 0.089963
Standard error............: 0.048028
Odds ratio (effect size)..: 1.094
Lower 95% CI..............: 0.996
Upper 95% CI..............: 1.202
T-value...................: 1.873132
P-value...................: 0.06173309
R^2.......................: 0.126494
Adjusted r^2..............: 0.112175
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing MDC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.07567 0.32640 -0.29952 -0.88072 -0.80133 -0.30228
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7288 -0.5956 -0.0938 0.5927 2.9163
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.1336342 0.3942673 -0.339 0.73484
currentDF[, TRAIT] 0.0432840 0.0497035 0.871 0.38438
Age 0.0003512 0.0053519 0.066 0.94772
Gendermale 0.3284606 0.0996351 3.297 0.00107 **
ORdate_year2003 -0.2771732 0.1666377 -1.663 0.09706 .
ORdate_year2004 -0.8449639 0.1578813 -5.352 1.50e-07 ***
ORdate_year2005 -0.7570794 0.1616350 -4.684 3.92e-06 ***
ORdate_year2006 -0.2483746 0.2535222 -0.980 0.32785
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9091 on 383 degrees of freedom
Multiple R-squared: 0.1365, Adjusted R-squared: 0.1207
F-statistic: 8.646 on 7 and 383 DF, p-value: 7.344e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MDC_rank
Effect size...............: 0.043284
Standard error............: 0.049703
Odds ratio (effect size)..: 1.044
Lower 95% CI..............: 0.947
Upper 95% CI..............: 1.151
T-value...................: 0.870844
P-value...................: 0.3843847
R^2.......................: 0.136452
Adjusted r^2..............: 0.12067
Sample size of AE DB......: 2423
Sample size of model......: 391
Missing data %............: 83.86298
- processing OPG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.04623 0.12503 0.24702 -0.32636 -0.79840 -0.80246 -0.30577
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.6172 -0.6066 -0.1085 0.5794 2.7997
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.297532 0.365469 -0.814 0.41604
currentDF[, TRAIT] 0.127740 0.043423 2.942 0.00344 **
Age 0.003717 0.004993 0.745 0.45696
Gendermale 0.244585 0.093039 2.629 0.00888 **
ORdate_year2003 -0.316687 0.154422 -2.051 0.04090 *
ORdate_year2004 -0.796915 0.147727 -5.394 1.14e-07 ***
ORdate_year2005 -0.805321 0.150157 -5.363 1.34e-07 ***
ORdate_year2006 -0.317866 0.241195 -1.318 0.18825
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8917 on 426 degrees of freedom
Multiple R-squared: 0.1368, Adjusted R-squared: 0.1226
F-statistic: 9.642 on 7 and 426 DF, p-value: 3.762e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: OPG_rank
Effect size...............: 0.12774
Standard error............: 0.043423
Odds ratio (effect size)..: 1.136
Lower 95% CI..............: 1.044
Upper 95% CI..............: 1.237
T-value...................: 2.941773
P-value...................: 0.003441462
R^2.......................: 0.13677
Adjusted r^2..............: 0.122586
Sample size of AE DB......: 2423
Sample size of model......: 434
Missing data %............: 82.08832
- processing sICAM1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.07944 0.10864 0.27052 -0.34718 -0.78735 -0.75364 -0.19930
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7363 -0.5914 -0.0803 0.5445 2.9173
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.363338 0.368312 -0.986 0.32445
currentDF[, TRAIT] 0.113280 0.044144 2.566 0.01062 *
Age 0.004171 0.004997 0.835 0.40438
Gendermale 0.268962 0.092560 2.906 0.00385 **
ORdate_year2003 -0.337625 0.154081 -2.191 0.02898 *
ORdate_year2004 -0.784337 0.148955 -5.266 2.22e-07 ***
ORdate_year2005 -0.754685 0.151831 -4.971 9.68e-07 ***
ORdate_year2006 -0.208592 0.244228 -0.854 0.39354
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8928 on 427 degrees of freedom
Multiple R-squared: 0.1327, Adjusted R-squared: 0.1185
F-statistic: 9.333 on 7 and 427 DF, p-value: 8.936e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: sICAM1_rank
Effect size...............: 0.11328
Standard error............: 0.044144
Odds ratio (effect size)..: 1.12
Lower 95% CI..............: 1.027
Upper 95% CI..............: 1.221
T-value...................: 2.56617
P-value...................: 0.01062295
R^2.......................: 0.132693
Adjusted r^2..............: 0.118474
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing VEGFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.2285 0.2356 0.2565 -0.5968 -0.9910 -1.3456 -0.6431
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.6960 -0.5357 -0.1356 0.4521 3.0045
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.018436 0.381013 -0.048 0.961436
currentDF[, TRAIT] 0.234189 0.049923 4.691 3.87e-06 ***
Age 0.003656 0.005080 0.720 0.472226
Gendermale 0.257540 0.097150 2.651 0.008381 **
ORdate_year2003 -0.590396 0.173829 -3.396 0.000759 ***
ORdate_year2004 -0.993182 0.161727 -6.141 2.17e-09 ***
ORdate_year2005 -1.351845 0.182521 -7.407 9.29e-13 ***
ORdate_year2006 -0.659320 0.260041 -2.535 0.011653 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8432 on 360 degrees of freedom
Multiple R-squared: 0.1932, Adjusted R-squared: 0.1775
F-statistic: 12.32 on 7 and 360 DF, p-value: 3.85e-14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: VEGFA_rank
Effect size...............: 0.234189
Standard error............: 0.049923
Odds ratio (effect size)..: 1.264
Lower 95% CI..............: 1.146
Upper 95% CI..............: 1.394
T-value...................: 4.690963
P-value...................: 3.867726e-06
R^2.......................: 0.193197
Adjusted r^2..............: 0.177509
Sample size of AE DB......: 2423
Sample size of model......: 368
Missing data %............: 84.81222
- processing TGFB_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.01827 0.25057 -0.36599 -0.87411 -0.85523 -0.27745
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7221 -0.6233 -0.1101 0.5899 2.9391
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.108495 0.371195 -0.292 0.77021
currentDF[, TRAIT] 0.014792 0.045788 0.323 0.74682
Age 0.001772 0.005065 0.350 0.72656
Gendermale 0.251277 0.095532 2.630 0.00885 **
ORdate_year2003 -0.359726 0.155739 -2.310 0.02139 *
ORdate_year2004 -0.863320 0.152596 -5.658 2.86e-08 ***
ORdate_year2005 -0.848229 0.153845 -5.514 6.18e-08 ***
ORdate_year2006 -0.277015 0.256095 -1.082 0.28002
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9047 on 417 degrees of freedom
Multiple R-squared: 0.1264, Adjusted R-squared: 0.1117
F-statistic: 8.619 on 7 and 417 DF, p-value: 6.915e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: TGFB_rank
Effect size...............: 0.014792
Standard error............: 0.045788
Odds ratio (effect size)..: 1.015
Lower 95% CI..............: 0.928
Upper 95% CI..............: 1.11
T-value...................: 0.32305
P-value...................: 0.7468193
R^2.......................: 0.126396
Adjusted r^2..............: 0.111731
Sample size of AE DB......: 2423
Sample size of model......: 425
Missing data %............: 82.45976
- processing MMP2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.06081 0.32224 -0.35983 -0.86215 -0.87074 -0.48698
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7657 -0.6102 -0.1075 0.5792 3.0597
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.201488 0.369566 -0.545 0.58590
currentDF[, TRAIT] 0.022531 0.046022 0.490 0.62469
Age 0.001966 0.005038 0.390 0.69660
Gendermale 0.327413 0.095066 3.444 0.00063 ***
ORdate_year2003 -0.356895 0.151544 -2.355 0.01898 *
ORdate_year2004 -0.855431 0.146143 -5.853 9.69e-09 ***
ORdate_year2005 -0.867330 0.149627 -5.797 1.33e-08 ***
ORdate_year2006 -0.493806 0.470181 -1.050 0.29421
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9043 on 422 degrees of freedom
Multiple R-squared: 0.1328, Adjusted R-squared: 0.1184
F-statistic: 9.231 on 7 and 422 DF, p-value: 1.215e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MMP2_rank
Effect size...............: 0.022531
Standard error............: 0.046022
Odds ratio (effect size)..: 1.023
Lower 95% CI..............: 0.935
Upper 95% CI..............: 1.119
T-value...................: 0.489571
P-value...................: 0.6246919
R^2.......................: 0.132784
Adjusted r^2..............: 0.118398
Sample size of AE DB......: 2423
Sample size of model......: 430
Missing data %............: 82.25341
- processing MMP8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.02663 0.18271 0.25553 -0.34853 -0.83535 -0.87806 -0.33133
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9768 -0.5620 -0.1048 0.4903 3.0030
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.153520 0.359976 -0.426 0.66998
currentDF[, TRAIT] 0.182809 0.044838 4.077 5.45e-05 ***
Age 0.001879 0.004926 0.381 0.70311
Gendermale 0.255359 0.094103 2.714 0.00693 **
ORdate_year2003 -0.344792 0.148710 -2.319 0.02090 *
ORdate_year2004 -0.835546 0.142867 -5.848 9.96e-09 ***
ORdate_year2005 -0.880063 0.146436 -6.010 4.02e-09 ***
ORdate_year2006 -0.337578 0.462883 -0.729 0.46623
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8872 on 422 degrees of freedom
Multiple R-squared: 0.1652, Adjusted R-squared: 0.1513
F-statistic: 11.93 on 7 and 422 DF, p-value: 6.694e-14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MMP8_rank
Effect size...............: 0.182809
Standard error............: 0.044838
Odds ratio (effect size)..: 1.201
Lower 95% CI..............: 1.1
Upper 95% CI..............: 1.311
T-value...................: 4.077065
P-value...................: 5.451683e-05
R^2.......................: 0.165175
Adjusted r^2..............: 0.151327
Sample size of AE DB......: 2423
Sample size of model......: 430
Missing data %............: 82.25341
- processing MMP9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.07336 0.10703 0.30046 -0.35447 -0.82421 -0.84074 -0.40683
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8791 -0.5835 -0.1225 0.5501 2.9562
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.163216 0.364485 -0.448 0.65453
currentDF[, TRAIT] 0.106611 0.044079 2.419 0.01600 *
Age 0.001331 0.004991 0.267 0.78984
Gendermale 0.300443 0.094253 3.188 0.00154 **
ORdate_year2003 -0.351854 0.150554 -2.337 0.01990 *
ORdate_year2004 -0.824512 0.145346 -5.673 2.61e-08 ***
ORdate_year2005 -0.842269 0.148786 -5.661 2.79e-08 ***
ORdate_year2006 -0.411631 0.468292 -0.879 0.37990
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8983 on 422 degrees of freedom
Multiple R-squared: 0.1442, Adjusted R-squared: 0.13
F-statistic: 10.15 on 7 and 422 DF, p-value: 9.144e-12
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MMP9_rank
Effect size...............: 0.106611
Standard error............: 0.044079
Odds ratio (effect size)..: 1.113
Lower 95% CI..............: 1.02
Upper 95% CI..............: 1.213
T-value...................: 2.418635
P-value...................: 0.01600115
R^2.......................: 0.144155
Adjusted r^2..............: 0.129958
Sample size of AE DB......: 2423
Sample size of model......: 430
Missing data %............: 82.25341
Analysis of MCP1_rank.
- processing IL2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.23773 -0.07545 0.21479 -0.19791 -0.58597 -0.39400 0.68202
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4273 -0.6679 -0.0129 0.6118 2.8244
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.640133 0.394316 1.623 0.1053
currentDF[, TRAIT] -0.078116 0.049504 -1.578 0.1154
Age -0.006023 0.005498 -1.095 0.2740
Gendermale 0.220644 0.107073 2.061 0.0400 *
ORdate_year2003 -0.207358 0.153920 -1.347 0.1787
ORdate_year2004 -0.586180 0.147309 -3.979 8.21e-05 ***
ORdate_year2005 -0.383846 0.170483 -2.252 0.0249 *
ORdate_year2006 0.731571 0.978514 0.748 0.4551
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9597 on 400 degrees of freedom
Multiple R-squared: 0.06511, Adjusted R-squared: 0.04875
F-statistic: 3.98 on 7 and 400 DF, p-value: 0.0003201
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL2_rank
Effect size...............: -0.078116
Standard error............: 0.049504
Odds ratio (effect size)..: 0.925
Lower 95% CI..............: 0.839
Upper 95% CI..............: 1.019
T-value...................: -1.57798
P-value...................: 0.1153606
R^2.......................: 0.065109
Adjusted r^2..............: 0.048748
Sample size of AE DB......: 2423
Sample size of model......: 408
Missing data %............: 83.16137
- processing IL4_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005
0.1186 0.2997 -0.1273 -0.5327 -0.3911
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4070 -0.6412 0.0020 0.6187 2.8442
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.604430 0.415935 1.453 0.147018
currentDF[, TRAIT] -0.047147 0.051830 -0.910 0.363597
Age -0.006807 0.005802 -1.173 0.241494
Gendermale 0.297097 0.112746 2.635 0.008764 **
ORdate_year2003 -0.170821 0.157936 -1.082 0.280141
ORdate_year2004 -0.561215 0.150490 -3.729 0.000222 ***
ORdate_year2005 -0.394410 0.188894 -2.088 0.037479 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9648 on 371 degrees of freedom
Multiple R-squared: 0.06876, Adjusted R-squared: 0.0537
F-statistic: 4.566 on 6 and 371 DF, p-value: 0.0001741
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL4_rank
Effect size...............: -0.047147
Standard error............: 0.05183
Odds ratio (effect size)..: 0.954
Lower 95% CI..............: 0.862
Upper 95% CI..............: 1.056
T-value...................: -0.909652
P-value...................: 0.3635968
R^2.......................: 0.068764
Adjusted r^2..............: 0.053703
Sample size of AE DB......: 2423
Sample size of model......: 378
Missing data %............: 84.39951
- processing IL5_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005
0.787848 -0.101836 -0.008171 0.298126 -0.277882 -0.619323 -0.414797
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5357 -0.6345 -0.0491 0.6373 2.7831
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.787848 0.398694 1.976 0.04884 *
currentDF[, TRAIT] -0.101836 0.050466 -2.018 0.04428 *
Age -0.008171 0.005600 -1.459 0.14530
Gendermale 0.298126 0.107411 2.776 0.00577 **
ORdate_year2003 -0.277882 0.156245 -1.778 0.07609 .
ORdate_year2004 -0.619323 0.149677 -4.138 4.29e-05 ***
ORdate_year2005 -0.414797 0.178202 -2.328 0.02043 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9585 on 394 degrees of freedom
Multiple R-squared: 0.07263, Adjusted R-squared: 0.0585
F-statistic: 5.143 on 6 and 394 DF, p-value: 4.186e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL5_rank
Effect size...............: -0.101836
Standard error............: 0.050466
Odds ratio (effect size)..: 0.903
Lower 95% CI..............: 0.818
Upper 95% CI..............: 0.997
T-value...................: -2.017907
P-value...................: 0.0442783
R^2.......................: 0.072625
Adjusted r^2..............: 0.058503
Sample size of AE DB......: 2423
Sample size of model......: 401
Missing data %............: 83.45027
- processing IL6_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.2330 0.2028 -0.1790 -0.5369 -0.3788 0.5239
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2674 -0.6405 -0.0141 0.6563 2.8255
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.589074 0.392582 1.501 0.134239
currentDF[, TRAIT] 0.046531 0.049717 0.936 0.349856
Age -0.005678 0.005483 -1.036 0.301012
Gendermale 0.217170 0.106036 2.048 0.041178 *
ORdate_year2003 -0.161651 0.153331 -1.054 0.292375
ORdate_year2004 -0.509134 0.146148 -3.484 0.000547 ***
ORdate_year2005 -0.382093 0.162279 -2.355 0.019009 *
ORdate_year2006 0.617205 0.697171 0.885 0.376506
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.965 on 417 degrees of freedom
Multiple R-squared: 0.05567, Adjusted R-squared: 0.03982
F-statistic: 3.512 on 7 and 417 DF, p-value: 0.001126
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL6_rank
Effect size...............: 0.046531
Standard error............: 0.049717
Odds ratio (effect size)..: 1.048
Lower 95% CI..............: 0.95
Upper 95% CI..............: 1.155
T-value...................: 0.93592
P-value...................: 0.3498562
R^2.......................: 0.055672
Adjusted r^2..............: 0.03982
Sample size of AE DB......: 2423
Sample size of model......: 425
Missing data %............: 82.45976
- processing IL8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
1.09096 0.31676 -0.01062 0.22575 -0.25213 -0.65119 -0.67315 -0.28831
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2015 -0.5678 -0.0585 0.5428 2.9626
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.090957 0.374783 2.911 0.0038 **
currentDF[, TRAIT] 0.316761 0.047412 6.681 7.88e-11 ***
Age -0.010624 0.005194 -2.045 0.0415 *
Gendermale 0.225749 0.102252 2.208 0.0278 *
ORdate_year2003 -0.252125 0.147376 -1.711 0.0879 .
ORdate_year2004 -0.651189 0.140885 -4.622 5.12e-06 ***
ORdate_year2005 -0.673154 0.153726 -4.379 1.52e-05 ***
ORdate_year2006 -0.288313 0.471551 -0.611 0.5413
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9096 on 404 degrees of freedom
Multiple R-squared: 0.1601, Adjusted R-squared: 0.1455
F-statistic: 11 on 7 and 404 DF, p-value: 9.676e-13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL8_rank
Effect size...............: 0.316761
Standard error............: 0.047412
Odds ratio (effect size)..: 1.373
Lower 95% CI..............: 1.251
Upper 95% CI..............: 1.506
T-value...................: 6.681044
P-value...................: 7.883936e-11
R^2.......................: 0.160086
Adjusted r^2..............: 0.145533
Sample size of AE DB......: 2423
Sample size of model......: 412
Missing data %............: 82.99629
- processing IL9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.232647 0.286038 0.283661 -0.007248 -0.626776 -0.559779 -0.850795
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.08892 -0.59571 -0.09555 0.59332 2.87741
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.511215 0.330465 1.547 0.1225
currentDF[, TRAIT] 0.282709 0.039816 7.100 4.38e-12 ***
Age -0.004166 0.004590 -0.908 0.3645
Gendermale 0.285821 0.087700 3.259 0.0012 **
ORdate_year2003 -0.011194 0.139917 -0.080 0.9363
ORdate_year2004 -0.626793 0.129827 -4.828 1.84e-06 ***
ORdate_year2005 -0.556778 0.127907 -4.353 1.63e-05 ***
ORdate_year2006 -0.841940 0.204140 -4.124 4.37e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8798 on 492 degrees of freedom
Multiple R-squared: 0.1912, Adjusted R-squared: 0.1797
F-statistic: 16.61 on 7 and 492 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL9_rank
Effect size...............: 0.282709
Standard error............: 0.039816
Odds ratio (effect size)..: 1.327
Lower 95% CI..............: 1.227
Upper 95% CI..............: 1.434
T-value...................: 7.100414
P-value...................: 4.375948e-12
R^2.......................: 0.191183
Adjusted r^2..............: 0.179675
Sample size of AE DB......: 2423
Sample size of model......: 500
Missing data %............: 79.36442
- processing IL10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005
0.2131 -0.1360 0.2821 -0.2023 -0.6164 -0.3071
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5617 -0.6209 -0.0305 0.6394 2.7973
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.762090 0.423910 1.798 0.0731 .
currentDF[, TRAIT] -0.141857 0.055039 -2.577 0.0104 *
Age -0.008234 0.005945 -1.385 0.1669
Gendermale 0.297311 0.115413 2.576 0.0104 *
ORdate_year2003 -0.221598 0.161494 -1.372 0.1709
ORdate_year2004 -0.620611 0.154787 -4.009 7.44e-05 ***
ORdate_year2005 -0.299938 0.207840 -1.443 0.1499
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9691 on 351 degrees of freedom
Multiple R-squared: 0.08224, Adjusted R-squared: 0.06656
F-statistic: 5.242 on 6 and 351 DF, p-value: 3.456e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL10_rank
Effect size...............: -0.141857
Standard error............: 0.055039
Odds ratio (effect size)..: 0.868
Lower 95% CI..............: 0.779
Upper 95% CI..............: 0.967
T-value...................: -2.577419
P-value...................: 0.01036155
R^2.......................: 0.082244
Adjusted r^2..............: 0.066556
Sample size of AE DB......: 2423
Sample size of model......: 358
Missing data %............: 85.22493
- processing IL12_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005
0.14690 -0.08991 0.30101 -0.13899 -0.54113 -0.38584
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5763 -0.6244 -0.0410 0.6401 2.8125
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.525893 0.410614 1.281 0.201080
currentDF[, TRAIT] -0.093203 0.051567 -1.807 0.071504 .
Age -0.005705 0.005777 -0.988 0.324029
Gendermale 0.313353 0.111186 2.818 0.005086 **
ORdate_year2003 -0.153170 0.159413 -0.961 0.337258
ORdate_year2004 -0.542935 0.149427 -3.633 0.000319 ***
ORdate_year2005 -0.378422 0.189093 -2.001 0.046091 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9606 on 372 degrees of freedom
Multiple R-squared: 0.07115, Adjusted R-squared: 0.05617
F-statistic: 4.749 on 6 and 372 DF, p-value: 0.0001116
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL12_rank
Effect size...............: -0.093203
Standard error............: 0.051567
Odds ratio (effect size)..: 0.911
Lower 95% CI..............: 0.823
Upper 95% CI..............: 1.008
T-value...................: -1.807422
P-value...................: 0.07150421
R^2.......................: 0.07115
Adjusted r^2..............: 0.056169
Sample size of AE DB......: 2423
Sample size of model......: 379
Missing data %............: 84.35823
- processing IL13_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.4860 0.4238 0.2148 -0.4487 -0.8473 -0.8010 -0.9665
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.6611 -0.6241 -0.0900 0.5672 2.3808
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.594276 0.312390 1.902 0.057650 .
currentDF[, TRAIT] 0.422608 0.038335 11.024 < 2e-16 ***
Age -0.001631 0.004333 -0.376 0.706723
Gendermale 0.216010 0.082701 2.612 0.009250 **
ORdate_year2003 -0.450146 0.133610 -3.369 0.000807 ***
ORdate_year2004 -0.846148 0.128371 -6.591 1.03e-10 ***
ORdate_year2005 -0.798907 0.128238 -6.230 9.32e-10 ***
ORdate_year2006 -0.962546 0.202207 -4.760 2.48e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8725 on 547 degrees of freedom
Multiple R-squared: 0.2432, Adjusted R-squared: 0.2336
F-statistic: 25.12 on 7 and 547 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL13_rank
Effect size...............: 0.422608
Standard error............: 0.038335
Odds ratio (effect size)..: 1.526
Lower 95% CI..............: 1.415
Upper 95% CI..............: 1.645
T-value...................: 11.02412
P-value...................: 1.170157e-25
R^2.......................: 0.243241
Adjusted r^2..............: 0.233556
Sample size of AE DB......: 2423
Sample size of model......: 555
Missing data %............: 77.09451
- processing IL21_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.4387 0.3728 0.2114 -0.4034 -0.7932 -0.7389 -0.9087
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5993 -0.6378 -0.1010 0.5590 2.5330
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.649968 0.320645 2.027 0.04314 *
currentDF[, TRAIT] 0.370610 0.039292 9.432 < 2e-16 ***
Age -0.003178 0.004440 -0.716 0.47439
Gendermale 0.213465 0.085006 2.511 0.01232 *
ORdate_year2003 -0.406006 0.136693 -2.970 0.00311 **
ORdate_year2004 -0.791149 0.131613 -6.011 3.36e-09 ***
ORdate_year2005 -0.735019 0.131363 -5.595 3.48e-08 ***
ORdate_year2006 -0.901341 0.207964 -4.334 1.74e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8976 on 548 degrees of freedom
Multiple R-squared: 0.2041, Adjusted R-squared: 0.194
F-statistic: 20.08 on 7 and 548 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL21_rank
Effect size...............: 0.37061
Standard error............: 0.039292
Odds ratio (effect size)..: 1.449
Lower 95% CI..............: 1.341
Upper 95% CI..............: 1.565
T-value...................: 9.4322
P-value...................: 1.129816e-19
R^2.......................: 0.204135
Adjusted r^2..............: 0.193969
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing INFG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.1379 0.3658 -0.1946 -0.5717 -0.5673 -0.5662
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4603 -0.6395 -0.0161 0.6855 2.7926
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.507100 0.404130 1.255 0.210292
currentDF[, TRAIT] -0.049316 0.052098 -0.947 0.344424
Age -0.004991 0.005611 -0.889 0.374327
Gendermale 0.361757 0.108495 3.334 0.000935 ***
ORdate_year2003 -0.230189 0.156651 -1.469 0.142506
ORdate_year2004 -0.598529 0.149087 -4.015 7.12e-05 ***
ORdate_year2005 -0.618718 0.180008 -3.437 0.000650 ***
ORdate_year2006 -0.677516 0.707064 -0.958 0.338540
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9703 on 397 degrees of freedom
Multiple R-squared: 0.0796, Adjusted R-squared: 0.06337
F-statistic: 4.905 on 7 and 397 DF, p-value: 2.5e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: INFG_rank
Effect size...............: -0.049316
Standard error............: 0.052098
Odds ratio (effect size)..: 0.952
Lower 95% CI..............: 0.859
Upper 95% CI..............: 1.054
T-value...................: -0.946589
P-value...................: 0.3444242
R^2.......................: 0.079596
Adjusted r^2..............: 0.063368
Sample size of AE DB......: 2423
Sample size of model......: 405
Missing data %............: 83.28518
- processing TNFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.08689 0.26592 -0.10827 -0.47370 -0.31292 0.26338
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3908 -0.6402 0.0024 0.6017 2.8287
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.579893 0.418850 1.384 0.16706
currentDF[, TRAIT] -0.017853 0.054044 -0.330 0.74134
Age -0.007229 0.005834 -1.239 0.21610
Gendermale 0.275440 0.112481 2.449 0.01481 *
ORdate_year2003 -0.133271 0.165215 -0.807 0.42040
ORdate_year2004 -0.490148 0.158894 -3.085 0.00219 **
ORdate_year2005 -0.299262 0.190969 -1.567 0.11797
ORdate_year2006 0.306647 0.978617 0.313 0.75420
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9673 on 362 degrees of freedom
Multiple R-squared: 0.05655, Adjusted R-squared: 0.0383
F-statistic: 3.099 on 7 and 362 DF, p-value: 0.003464
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: TNFA_rank
Effect size...............: -0.017853
Standard error............: 0.054044
Odds ratio (effect size)..: 0.982
Lower 95% CI..............: 0.884
Upper 95% CI..............: 1.092
T-value...................: -0.330336
P-value...................: 0.7413369
R^2.......................: 0.056545
Adjusted r^2..............: 0.038302
Sample size of AE DB......: 2423
Sample size of model......: 370
Missing data %............: 84.72967
- processing MIF_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale
-0.1633 0.3773 0.2289
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2912 -0.5906 -0.0290 0.6494 2.7566
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.353780 0.330790 1.070 0.28531
currentDF[, TRAIT] 0.349057 0.045008 7.755 4.32e-14 ***
Age -0.004599 0.004536 -1.014 0.31106
Gendermale 0.245496 0.086824 2.828 0.00486 **
ORdate_year2003 -0.235012 0.138369 -1.698 0.08999 .
ORdate_year2004 -0.247919 0.139392 -1.779 0.07586 .
ORdate_year2005 -0.223086 0.140677 -1.586 0.11336
ORdate_year2006 -0.489561 0.219605 -2.229 0.02620 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9186 on 548 degrees of freedom
Multiple R-squared: 0.1664, Adjusted R-squared: 0.1558
F-statistic: 15.63 on 7 and 548 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MIF_rank
Effect size...............: 0.349057
Standard error............: 0.045008
Odds ratio (effect size)..: 1.418
Lower 95% CI..............: 1.298
Upper 95% CI..............: 1.548
T-value...................: 7.755397
P-value...................: 4.320345e-14
R^2.......................: 0.166419
Adjusted r^2..............: 0.155771
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing MCP1_rankattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsense
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
1.307e-34 1.000e+00 essentially perfect fit: summary may be unreliable
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.244e-16 -2.921e-17 -2.860e-18 1.824e-17 3.116e-15
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.913e-17 5.096e-17 -3.750e-01 0.7076
currentDF[, TRAIT] 1.000e+00 6.276e-18 1.593e+17 <2e-16 ***
Age 7.139e-19 7.022e-19 1.017e+00 0.3098
Gendermale 6.605e-18 1.353e-17 4.880e-01 0.6257
ORdate_year2003 -4.661e-17 2.145e-17 -2.173e+00 0.0302 *
ORdate_year2004 -3.662e-17 2.086e-17 -1.756e+00 0.0797 .
ORdate_year2005 -3.628e-17 2.094e-17 -1.733e+00 0.0837 .
ORdate_year2006 -2.147e-17 3.343e-17 -6.420e-01 0.5210
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.422e-16 on 548 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 3.921e+33 on 7 and 548 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MCP1_rank ' .essentially perfect fit: summary may be unreliableCollecting data.essentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableWe have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MCP1_rank
Effect size...............: 1
Standard error............: 0
Odds ratio (effect size)..: 2.718
Lower 95% CI..............: 2.718
Upper 95% CI..............: 2.718
T-value...................: 1.593436e+17
P-value...................: 0
R^2.......................: 1
Adjusted r^2..............: 1
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing MIP1a_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.32420 0.35362 0.20205 -0.03445 -0.67599 -0.62650 -0.80208
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.83885 -0.62719 -0.08645 0.55672 2.90268
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.654198 0.317480 2.061 0.0399 *
currentDF[, TRAIT] 0.350978 0.038504 9.115 < 2e-16 ***
Age -0.004948 0.004411 -1.122 0.2625
Gendermale 0.205436 0.084734 2.424 0.0157 *
ORdate_year2003 -0.040012 0.133760 -0.299 0.7650
ORdate_year2004 -0.675370 0.126380 -5.344 1.38e-07 ***
ORdate_year2005 -0.622285 0.124697 -4.990 8.31e-07 ***
ORdate_year2006 -0.791479 0.199092 -3.975 8.05e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8574 on 505 degrees of freedom
Multiple R-squared: 0.2297, Adjusted R-squared: 0.219
F-statistic: 21.51 on 7 and 505 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.350978
Standard error............: 0.038504
Odds ratio (effect size)..: 1.42
Lower 95% CI..............: 1.317
Upper 95% CI..............: 1.532
T-value...................: 9.115453
P-value...................: 1.845875e-18
R^2.......................: 0.229686
Adjusted r^2..............: 0.219008
Sample size of AE DB......: 2423
Sample size of model......: 513
Missing data %............: 78.8279
- processing RANTES_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.04641 0.34375 0.20851 0.05907 -0.10859 -0.17170 -0.49438
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3386 -0.5303 -0.0225 0.6081 3.0006
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0111118 0.3367301 -0.033 0.9737
currentDF[, TRAIT] 0.3430704 0.0443331 7.738 4.97e-14 ***
Age -0.0005196 0.0045628 -0.114 0.9094
Gendermale 0.2089363 0.0869434 2.403 0.0166 *
ORdate_year2003 0.0578970 0.1417289 0.409 0.6831
ORdate_year2004 -0.1094086 0.1449468 -0.755 0.4507
ORdate_year2005 -0.1720381 0.1421487 -1.210 0.2267
ORdate_year2006 -0.4940217 0.2181132 -2.265 0.0239 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9124 on 541 degrees of freedom
Multiple R-squared: 0.1693, Adjusted R-squared: 0.1585
F-statistic: 15.75 on 7 and 541 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: RANTES_rank
Effect size...............: 0.34307
Standard error............: 0.044333
Odds ratio (effect size)..: 1.409
Lower 95% CI..............: 1.292
Upper 95% CI..............: 1.537
T-value...................: 7.738468
P-value...................: 4.96533e-14
R^2.......................: 0.16929
Adjusted r^2..............: 0.158541
Sample size of AE DB......: 2423
Sample size of model......: 549
Missing data %............: 77.34214
- processing MIG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.4381 0.3169 0.2299 -0.2177 -0.8360 -0.8154 -1.1313
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3481 -0.6193 -0.0581 0.5754 2.6933
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.661797 0.329185 2.010 0.04489 *
currentDF[, TRAIT] 0.313672 0.041792 7.506 2.55e-13 ***
Age -0.003376 0.004576 -0.738 0.46094
Gendermale 0.232023 0.086842 2.672 0.00777 **
ORdate_year2003 -0.220470 0.138558 -1.591 0.11216
ORdate_year2004 -0.832642 0.136117 -6.117 1.84e-09 ***
ORdate_year2005 -0.809837 0.136012 -5.954 4.72e-09 ***
ORdate_year2006 -1.121261 0.213135 -5.261 2.07e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9115 on 538 degrees of freedom
Multiple R-squared: 0.1691, Adjusted R-squared: 0.1583
F-statistic: 15.65 on 7 and 538 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MIG_rank
Effect size...............: 0.313672
Standard error............: 0.041792
Odds ratio (effect size)..: 1.368
Lower 95% CI..............: 1.261
Upper 95% CI..............: 1.485
T-value...................: 7.5056
P-value...................: 2.55159e-13
R^2.......................: 0.169134
Adjusted r^2..............: 0.158323
Sample size of AE DB......: 2423
Sample size of model......: 546
Missing data %............: 77.46595
- processing IP10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.3397 0.4309 0.2453 -0.2507 -0.7034 -0.6386 -0.7495
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3459 -0.6311 -0.0563 0.5412 2.2450
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.489739 0.320834 1.526 0.127544
currentDF[, TRAIT] 0.429691 0.039130 10.981 < 2e-16 ***
Age -0.002248 0.004463 -0.504 0.614648
Gendermale 0.246891 0.083971 2.940 0.003435 **
ORdate_year2003 -0.253849 0.133856 -1.896 0.058492 .
ORdate_year2004 -0.703339 0.125598 -5.600 3.58e-08 ***
ORdate_year2005 -0.637337 0.125444 -5.081 5.36e-07 ***
ORdate_year2006 -0.745594 0.206736 -3.607 0.000342 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8488 on 489 degrees of freedom
Multiple R-squared: 0.2646, Adjusted R-squared: 0.2541
F-statistic: 25.14 on 7 and 489 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IP10_rank
Effect size...............: 0.429691
Standard error............: 0.03913
Odds ratio (effect size)..: 1.537
Lower 95% CI..............: 1.423
Upper 95% CI..............: 1.659
T-value...................: 10.98102
P-value...................: 3.169245e-25
R^2.......................: 0.26462
Adjusted r^2..............: 0.254093
Sample size of AE DB......: 2423
Sample size of model......: 497
Missing data %............: 79.48824
- processing Eotaxin1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.4204 0.3481 0.1904 -0.2771 -0.7801 -0.7335 -0.9387
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4856 -0.6401 -0.0844 0.5754 2.7397
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.701727 0.324032 2.166 0.0308 *
currentDF[, TRAIT] 0.345928 0.039755 8.701 < 2e-16 ***
Age -0.004225 0.004480 -0.943 0.3461
Gendermale 0.193182 0.086166 2.242 0.0254 *
ORdate_year2003 -0.281650 0.136824 -2.058 0.0400 *
ORdate_year2004 -0.777898 0.133026 -5.848 8.57e-09 ***
ORdate_year2005 -0.728662 0.132822 -5.486 6.29e-08 ***
ORdate_year2006 -0.928724 0.210172 -4.419 1.20e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.907 on 548 degrees of freedom
Multiple R-squared: 0.1872, Adjusted R-squared: 0.1768
F-statistic: 18.03 on 7 and 548 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.345928
Standard error............: 0.039755
Odds ratio (effect size)..: 1.413
Lower 95% CI..............: 1.307
Upper 95% CI..............: 1.528
T-value...................: 8.701445
P-value...................: 3.840103e-17
R^2.......................: 0.187227
Adjusted r^2..............: 0.176844
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing TARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.07889 0.27558 0.13538 0.17052 -0.15874 -0.09947 -0.49110
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.05279 -0.59464 -0.05743 0.62497 2.59052
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.071116 0.381059 0.187 0.8520
currentDF[, TRAIT] 0.273442 0.044616 6.129 1.87e-09 ***
Age -0.002226 0.004810 -0.463 0.6437
Gendermale 0.136007 0.093154 1.460 0.1449
ORdate_year2003 0.167415 0.209948 0.797 0.4256
ORdate_year2004 -0.159377 0.207434 -0.768 0.4427
ORdate_year2005 -0.098963 0.208704 -0.474 0.6356
ORdate_year2006 -0.486671 0.264908 -1.837 0.0668 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9185 on 473 degrees of freedom
Multiple R-squared: 0.1246, Adjusted R-squared: 0.1116
F-statistic: 9.616 on 7 and 473 DF, p-value: 3.359e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: TARC_rank
Effect size...............: 0.273442
Standard error............: 0.044616
Odds ratio (effect size)..: 1.314
Lower 95% CI..............: 1.204
Upper 95% CI..............: 1.435
T-value...................: 6.128727
P-value...................: 1.868227e-09
R^2.......................: 0.12458
Adjusted r^2..............: 0.111624
Sample size of AE DB......: 2423
Sample size of model......: 481
Missing data %............: 80.14858
- processing PARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.421239 0.439763 -0.006743 0.283718 -0.173869 -0.130971 -0.223108 -0.552767
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.03092 -0.57322 -0.03825 0.57915 2.12587
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.421239 0.314937 1.338 0.181603
currentDF[, TRAIT] 0.439763 0.040830 10.771 < 2e-16 ***
Age -0.006743 0.004335 -1.556 0.120354
Gendermale 0.283718 0.083016 3.418 0.000679 ***
ORdate_year2003 -0.173869 0.132451 -1.313 0.189833
ORdate_year2004 -0.130971 0.133632 -0.980 0.327475
ORdate_year2005 -0.223108 0.131603 -1.695 0.090584 .
ORdate_year2006 -0.552767 0.206356 -2.679 0.007613 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8791 on 548 degrees of freedom
Multiple R-squared: 0.2365, Adjusted R-squared: 0.2268
F-statistic: 24.26 on 7 and 548 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: PARC_rank
Effect size...............: 0.439763
Standard error............: 0.04083
Odds ratio (effect size)..: 1.552
Lower 95% CI..............: 1.433
Upper 95% CI..............: 1.682
T-value...................: 10.77062
P-value...................: 1.1435e-24
R^2.......................: 0.236545
Adjusted r^2..............: 0.226793
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing MDC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.01437 0.37784 0.28981 0.10466 -0.29365 -0.24619 -0.50856
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.9657 -0.5977 -0.1212 0.5546 3.4079
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.225201 0.320842 0.702 0.483065
currentDF[, TRAIT] 0.375756 0.040316 9.320 < 2e-16 ***
Age -0.003568 0.004418 -0.808 0.419635
Gendermale 0.292207 0.084666 3.451 0.000605 ***
ORdate_year2003 0.098737 0.136451 0.724 0.469644
ORdate_year2004 -0.295157 0.129966 -2.271 0.023569 *
ORdate_year2005 -0.245186 0.129828 -1.889 0.059532 .
ORdate_year2006 -0.502604 0.203940 -2.464 0.014057 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8594 on 500 degrees of freedom
Multiple R-squared: 0.2338, Adjusted R-squared: 0.223
F-statistic: 21.79 on 7 and 500 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MDC_rank
Effect size...............: 0.375756
Standard error............: 0.040316
Odds ratio (effect size)..: 1.456
Lower 95% CI..............: 1.345
Upper 95% CI..............: 1.576
T-value...................: 9.320353
P-value...................: 3.710608e-19
R^2.......................: 0.233766
Adjusted r^2..............: 0.223039
Sample size of AE DB......: 2423
Sample size of model......: 508
Missing data %............: 79.03426
- processing OPG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.2527 0.5883 0.1680 -0.1905 -0.3841 -0.5938 -1.0461
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.6138 -0.5384 -0.0730 0.4763 2.5302
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.341906 0.279821 1.222 0.222282
currentDF[, TRAIT] 0.587533 0.034551 17.005 < 2e-16 ***
Age -0.001335 0.003869 -0.345 0.730114
Gendermale 0.168952 0.074056 2.281 0.022908 *
ORdate_year2003 -0.192275 0.117688 -1.634 0.102882
ORdate_year2004 -0.384088 0.113283 -3.391 0.000748 ***
ORdate_year2005 -0.592566 0.113195 -5.235 2.36e-07 ***
ORdate_year2006 -1.042826 0.180925 -5.764 1.37e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7801 on 547 degrees of freedom
Multiple R-squared: 0.3949, Adjusted R-squared: 0.3872
F-statistic: 51.01 on 7 and 547 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: OPG_rank
Effect size...............: 0.587533
Standard error............: 0.034551
Odds ratio (effect size)..: 1.8
Lower 95% CI..............: 1.682
Upper 95% CI..............: 1.926
T-value...................: 17.00458
P-value...................: 2.268881e-52
R^2.......................: 0.394949
Adjusted r^2..............: 0.387206
Sample size of AE DB......: 2423
Sample size of model......: 555
Missing data %............: 77.09451
- processing sICAM1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale
-0.1740 0.6658 0.2313
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.02787 -0.39146 0.02809 0.43169 2.24289
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.078314 0.265721 -0.295 0.768318
currentDF[, TRAIT] 0.661086 0.033110 19.966 < 2e-16 ***
Age 0.001486 0.003652 0.407 0.684221
Gendermale 0.238104 0.069548 3.424 0.000664 ***
ORdate_year2003 -0.255982 0.110904 -2.308 0.021363 *
ORdate_year2004 -0.180484 0.108220 -1.668 0.095938 .
ORdate_year2005 -0.241434 0.108100 -2.233 0.025923 *
ORdate_year2006 -0.314496 0.173169 -1.816 0.069899 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7362 on 548 degrees of freedom
Multiple R-squared: 0.4645, Adjusted R-squared: 0.4576
F-statistic: 67.9 on 7 and 548 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: sICAM1_rank
Effect size...............: 0.661086
Standard error............: 0.03311
Odds ratio (effect size)..: 1.937
Lower 95% CI..............: 1.815
Upper 95% CI..............: 2.067
T-value...................: 19.96615
P-value...................: 4.67076e-67
R^2.......................: 0.46449
Adjusted r^2..............: 0.45765
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing VEGFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.6085 0.3336 0.2048 -0.5618 -0.7468 -1.1147 -1.3753
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1762 -0.6140 -0.0826 0.5914 2.7511
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.014833 0.369597 2.746 0.006282 **
currentDF[, TRAIT] 0.335794 0.050085 6.704 6.16e-11 ***
Age -0.006056 0.004966 -1.219 0.223332
Gendermale 0.203733 0.097164 2.097 0.036577 *
ORdate_year2003 -0.567203 0.168122 -3.374 0.000807 ***
ORdate_year2004 -0.742556 0.157140 -4.725 3.09e-06 ***
ORdate_year2005 -1.107021 0.174867 -6.331 5.98e-10 ***
ORdate_year2006 -1.360111 0.241951 -5.621 3.35e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9109 on 444 degrees of freedom
Multiple R-squared: 0.1589, Adjusted R-squared: 0.1456
F-statistic: 11.98 on 7 and 444 DF, p-value: 5.01e-14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: VEGFA_rank
Effect size...............: 0.335794
Standard error............: 0.050085
Odds ratio (effect size)..: 1.399
Lower 95% CI..............: 1.268
Upper 95% CI..............: 1.543
T-value...................: 6.70445
P-value...................: 6.159075e-11
R^2.......................: 0.15888
Adjusted r^2..............: 0.145619
Sample size of AE DB......: 2423
Sample size of model......: 452
Missing data %............: 81.34544
- processing TGFB_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.2248 0.1020 0.2761 -0.1992 -0.5585 -0.5377 -0.8156
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5013 -0.5962 -0.0076 0.6394 2.6793
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.458565 0.348914 1.314 0.189337
currentDF[, TRAIT] 0.100342 0.043610 2.301 0.021791 *
Age -0.003498 0.004815 -0.727 0.467789
Gendermale 0.277754 0.092831 2.992 0.002903 **
ORdate_year2003 -0.202797 0.146972 -1.380 0.168230
ORdate_year2004 -0.558271 0.143734 -3.884 0.000116 ***
ORdate_year2005 -0.534522 0.142094 -3.762 0.000188 ***
ORdate_year2006 -0.806634 0.233584 -3.453 0.000599 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9554 on 520 degrees of freedom
Multiple R-squared: 0.08715, Adjusted R-squared: 0.07486
F-statistic: 7.092 on 7 and 520 DF, p-value: 4.185e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: TGFB_rank
Effect size...............: 0.100342
Standard error............: 0.04361
Odds ratio (effect size)..: 1.106
Lower 95% CI..............: 1.015
Upper 95% CI..............: 1.204
T-value...................: 2.300911
P-value...................: 0.0217914
R^2.......................: 0.087149
Adjusted r^2..............: 0.07486
Sample size of AE DB......: 2423
Sample size of model......: 528
Missing data %............: 78.20883
- processing MMP2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.1192 0.3282 0.3644 -0.2303 -0.4572 -0.4580 -0.5866
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4693 -0.5457 0.0027 0.5762 2.7358
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.479497 0.333210 1.439 0.150742
currentDF[, TRAIT] 0.325955 0.040684 8.012 7.40e-15 ***
Age -0.005375 0.004598 -1.169 0.242896
Gendermale 0.366462 0.088607 4.136 4.12e-05 ***
ORdate_year2003 -0.236424 0.137139 -1.724 0.085304 .
ORdate_year2004 -0.456781 0.132423 -3.449 0.000607 ***
ORdate_year2005 -0.454338 0.133284 -3.409 0.000703 ***
ORdate_year2006 -0.567570 0.389467 -1.457 0.145635
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9104 on 523 degrees of freedom
Multiple R-squared: 0.1708, Adjusted R-squared: 0.1597
F-statistic: 15.39 on 7 and 523 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MMP2_rank
Effect size...............: 0.325955
Standard error............: 0.040684
Odds ratio (effect size)..: 1.385
Lower 95% CI..............: 1.279
Upper 95% CI..............: 1.5
T-value...................: 8.011938
P-value...................: 7.395861e-15
R^2.......................: 0.170824
Adjusted r^2..............: 0.159726
Sample size of AE DB......: 2423
Sample size of model......: 531
Missing data %............: 78.08502
- processing MMP8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.847605 0.430758 -0.007942 0.139867 -0.183364 -0.512651 -0.582720 -0.402703
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3134 -0.4926 0.0184 0.5361 2.9610
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.847605 0.316046 2.682 0.00755 **
currentDF[, TRAIT] 0.430758 0.038316 11.242 < 2e-16 ***
Age -0.007942 0.004368 -1.818 0.06959 .
Gendermale 0.139867 0.085118 1.643 0.10094
ORdate_year2003 -0.183364 0.130420 -1.406 0.16033
ORdate_year2004 -0.512651 0.125213 -4.094 4.91e-05 ***
ORdate_year2005 -0.582720 0.126204 -4.617 4.90e-06 ***
ORdate_year2006 -0.402703 0.370833 -1.086 0.27800
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8657 on 523 degrees of freedom
Multiple R-squared: 0.2502, Adjusted R-squared: 0.2402
F-statistic: 24.94 on 7 and 523 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MMP8_rank
Effect size...............: 0.430758
Standard error............: 0.038316
Odds ratio (effect size)..: 1.538
Lower 95% CI..............: 1.427
Upper 95% CI..............: 1.658
T-value...................: 11.24238
P-value...................: 2.05252e-26
R^2.......................: 0.250244
Adjusted r^2..............: 0.240209
Sample size of AE DB......: 2423
Sample size of model......: 531
Missing data %............: 78.08502
- processing MMP9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.88945 0.56190 -0.01073 0.20400 -0.17971 -0.37281 -0.41489 -0.24740
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1926 -0.4989 -0.0247 0.5016 2.8649
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.88945 0.28901 3.078 0.002196 **
currentDF[, TRAIT] 0.56190 0.03533 15.904 < 2e-16 ***
Age -0.01073 0.00400 -2.682 0.007556 **
Gendermale 0.20400 0.07701 2.649 0.008314 **
ORdate_year2003 -0.17971 0.11925 -1.507 0.132430
ORdate_year2004 -0.37281 0.11516 -3.237 0.001283 **
ORdate_year2005 -0.41489 0.11572 -3.585 0.000368 ***
ORdate_year2006 -0.24740 0.33946 -0.729 0.466453
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7916 on 522 degrees of freedom
Multiple R-squared: 0.3728, Adjusted R-squared: 0.3644
F-statistic: 44.32 on 7 and 522 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MMP9_rank
Effect size...............: 0.561903
Standard error............: 0.035332
Odds ratio (effect size)..: 1.754
Lower 95% CI..............: 1.637
Upper 95% CI..............: 1.88
T-value...................: 15.9036
P-value...................: 9.991686e-47
R^2.......................: 0.37279
Adjusted r^2..............: 0.364379
Sample size of AE DB......: 2423
Sample size of model......: 530
Missing data %............: 78.12629 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)DT::datatable(GLM.results)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.MCP1_Plaque.Cytokines_Plaques.RANK.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Model 2
In this model we correct for Age, Gender, year of surgery, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of plaque cytokines as a function of plasma/plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(proteins_of_interest_rank)) {
TRAIT = proteins_of_interest_rank[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year +
Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose,
data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ug_2015_rank.
- processing IL2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year + SmokerStatus +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 SmokerStatusEx-smoker SmokerStatusNever smoked
0.5109 -0.1400 -0.8350 -0.7065 1.6447 -0.1352 0.2434
Med.Statin.LLDyes
-0.2185
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.61413 -0.69900 -0.03439 0.51884 2.83588
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.490689 1.319567 -0.372 0.710270
currentDF[, TRAIT] 0.034099 0.059900 0.569 0.569611
Age 0.001970 0.007243 0.272 0.785844
Gendermale 0.121135 0.124708 0.971 0.332175
ORdate_year2003 -0.114564 0.178547 -0.642 0.521606
ORdate_year2004 -0.782038 0.170102 -4.597 6.38e-06 ***
ORdate_year2005 -0.715123 0.205754 -3.476 0.000587 ***
ORdate_year2006 1.716493 1.012707 1.695 0.091150 .
Hypertension.compositeyes -0.150001 0.172191 -0.871 0.384400
DiabetesStatusDiabetes 0.061145 0.143586 0.426 0.670538
SmokerStatusEx-smoker -0.133745 0.127313 -1.051 0.294349
SmokerStatusNever smoked 0.330736 0.202754 1.631 0.103923
Med.Statin.LLDyes -0.222064 0.127372 -1.743 0.082312 .
Med.all.antiplateletyes -0.234088 0.210408 -1.113 0.266819
GFR_MDRD 0.001344 0.003271 0.411 0.681492
BMI -0.008971 0.017123 -0.524 0.600714
MedHx_CVDyes 0.162562 0.119244 1.363 0.173846
stenose50-70% 0.681362 1.045406 0.652 0.515064
stenose70-90% 1.201212 0.991812 1.211 0.226825
stenose90-99% 1.114058 0.990302 1.125 0.261526
stenose100% (Occlusion) 0.289608 1.173551 0.247 0.805252
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9664 on 292 degrees of freedom
Multiple R-squared: 0.1938, Adjusted R-squared: 0.1386
F-statistic: 3.51 on 20 and 292 DF, p-value: 1.255e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL2_rank
Effect size...............: 0.034099
Standard error............: 0.0599
Odds ratio (effect size)..: 1.035
Lower 95% CI..............: 0.92
Upper 95% CI..............: 1.164
T-value...................: 0.569269
P-value...................: 0.5696114
R^2.......................: 0.193816
Adjusted r^2..............: 0.138598
Sample size of AE DB......: 2423
Sample size of model......: 313
Missing data %............: 87.08213
- processing IL4_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year + SmokerStatus +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) ORdate_year2003 ORdate_year2004 ORdate_year2005 SmokerStatusEx-smoker SmokerStatusNever smoked Med.Statin.LLDyes
0.5189 -0.1103 -0.8637 -0.4326 -0.1529 0.3732 -0.2269
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.67791 -0.69959 -0.06449 0.56253 2.78634
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.2783842 0.9048264 0.308 0.7586
currentDF[, TRAIT] -0.0236622 0.0618121 -0.383 0.7022
Age 0.0025034 0.0074455 0.336 0.7370
Gendermale 0.1304062 0.1301565 1.002 0.3173
ORdate_year2003 -0.1203048 0.1804143 -0.667 0.5055
ORdate_year2004 -0.8434118 0.1730704 -4.873 1.88e-06 ***
ORdate_year2005 -0.4425205 0.2295243 -1.928 0.0549 .
Hypertension.compositeyes -0.1821757 0.1792700 -1.016 0.3104
DiabetesStatusDiabetes 0.0181687 0.1466374 0.124 0.9015
SmokerStatusEx-smoker -0.1666453 0.1290880 -1.291 0.1978
SmokerStatusNever smoked 0.4598087 0.2113153 2.176 0.0304 *
Med.Statin.LLDyes -0.2400635 0.1321952 -1.816 0.0705 .
Med.all.antiplateletyes -0.2294527 0.2199998 -1.043 0.2979
GFR_MDRD 0.0003073 0.0035539 0.086 0.9312
BMI -0.0068526 0.0175072 -0.391 0.6958
MedHx_CVDyes 0.1523842 0.1225876 1.243 0.2149
stenose70-90% 0.4785088 0.3315477 1.443 0.1501
stenose90-99% 0.4118991 0.3264759 1.262 0.2082
stenose100% (Occlusion) -0.4327537 0.6765643 -0.640 0.5230
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9544 on 269 degrees of freedom
Multiple R-squared: 0.1998, Adjusted R-squared: 0.1463
F-statistic: 3.732 on 18 and 269 DF, p-value: 1.08e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL4_rank
Effect size...............: -0.023662
Standard error............: 0.061812
Odds ratio (effect size)..: 0.977
Lower 95% CI..............: 0.865
Upper 95% CI..............: 1.102
T-value...................: -0.382809
P-value...................: 0.7021636
R^2.......................: 0.199831
Adjusted r^2..............: 0.146288
Sample size of AE DB......: 2423
Sample size of model......: 288
Missing data %............: 88.11391
- processing IL5_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year + SmokerStatus +
Med.Statin.LLD + MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) ORdate_year2003 ORdate_year2004 ORdate_year2005 SmokerStatusEx-smoker SmokerStatusNever smoked Med.Statin.LLDyes
0.4382 -0.1479 -0.8405 -0.5497 -0.1298 0.3460 -0.2609
MedHx_CVDyes
0.1800
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7811 -0.6451 -0.0920 0.5935 2.7294
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.435306 0.843345 0.516 0.60613
currentDF[, TRAIT] -0.043473 0.058372 -0.745 0.45702
Age 0.002956 0.007106 0.416 0.67776
Gendermale 0.142064 0.121655 1.168 0.24387
ORdate_year2003 -0.213492 0.175461 -1.217 0.22470
ORdate_year2004 -0.886702 0.167287 -5.300 2.3e-07 ***
ORdate_year2005 -0.622463 0.209496 -2.971 0.00322 **
Hypertension.compositeyes -0.216531 0.166899 -1.297 0.19554
DiabetesStatusDiabetes -0.048944 0.141400 -0.346 0.72949
SmokerStatusEx-smoker -0.156088 0.123674 -1.262 0.20794
SmokerStatusNever smoked 0.406877 0.204034 1.994 0.04708 *
Med.Statin.LLDyes -0.244126 0.123968 -1.969 0.04988 *
Med.all.antiplateletyes -0.230073 0.210803 -1.091 0.27600
GFR_MDRD 0.000101 0.003267 0.031 0.97537
BMI -0.002651 0.016251 -0.163 0.87055
MedHx_CVDyes 0.172674 0.115469 1.495 0.13590
stenose70-90% 0.272637 0.321464 0.848 0.39708
stenose90-99% 0.220770 0.319351 0.691 0.48993
stenose100% (Occlusion) -0.661689 0.662556 -0.999 0.31878
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9365 on 288 degrees of freedom
Multiple R-squared: 0.1933, Adjusted R-squared: 0.1429
F-statistic: 3.835 on 18 and 288 DF, p-value: 5.385e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL5_rank
Effect size...............: -0.043473
Standard error............: 0.058372
Odds ratio (effect size)..: 0.957
Lower 95% CI..............: 0.854
Upper 95% CI..............: 1.074
T-value...................: -0.744763
P-value...................: 0.4570223
R^2.......................: 0.193335
Adjusted r^2..............: 0.142918
Sample size of AE DB......: 2423
Sample size of model......: 307
Missing data %............: 87.32976
- processing IL6_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year + Med.Statin.LLD,
data = currentDF)
Coefficients:
(Intercept) ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes
0.5459 -0.1641 -0.8712 -0.9202 1.9023 -0.2677
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.61112 -0.71504 -0.08496 0.51120 3.03521
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.1009527 1.1464056 0.088 0.9299
currentDF[, TRAIT] 0.0710585 0.0600919 1.182 0.2380
Age 0.0061995 0.0075886 0.817 0.4146
Gendermale 0.1346947 0.1270148 1.060 0.2898
ORdate_year2003 -0.1299316 0.1823102 -0.713 0.4766
ORdate_year2004 -0.8142340 0.1718887 -4.737 3.38e-06 ***
ORdate_year2005 -0.9866472 0.2076762 -4.751 3.17e-06 ***
ORdate_year2006 1.6088860 1.0265449 1.567 0.1181
Hypertension.compositeyes -0.1429010 0.1771714 -0.807 0.4206
DiabetesStatusDiabetes 0.0570346 0.1471204 0.388 0.6985
SmokerStatusEx-smoker -0.1606526 0.1284518 -1.251 0.2120
SmokerStatusNever smoked 0.2723517 0.2083001 1.307 0.1921
Med.Statin.LLDyes -0.2451939 0.1287940 -1.904 0.0579 .
Med.all.antiplateletyes -0.1376455 0.2039125 -0.675 0.5002
GFR_MDRD 0.0005086 0.0034046 0.149 0.8814
BMI -0.0072265 0.0161571 -0.447 0.6550
MedHx_CVDyes 0.1634944 0.1199279 1.363 0.1738
stenose50-70% -0.2098951 0.7823723 -0.268 0.7887
stenose70-90% 0.3383113 0.7270341 0.465 0.6420
stenose90-99% 0.2194323 0.7234942 0.303 0.7619
stenose100% (Occlusion) -0.5086472 0.9675674 -0.526 0.5995
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9873 on 295 degrees of freedom
Multiple R-squared: 0.2083, Adjusted R-squared: 0.1546
F-statistic: 3.881 on 20 and 295 DF, p-value: 1.27e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL6_rank
Effect size...............: 0.071058
Standard error............: 0.060092
Odds ratio (effect size)..: 1.074
Lower 95% CI..............: 0.954
Upper 95% CI..............: 1.208
T-value...................: 1.182497
P-value...................: 0.2379609
R^2.......................: 0.208314
Adjusted r^2..............: 0.15464
Sample size of AE DB......: 2423
Sample size of model......: 316
Missing data %............: 86.95832
- processing IL8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes
0.6067 0.2407 -0.2748 -0.9597 -1.2944 -0.3376 -0.1992
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.96944 -0.67844 -0.05778 0.54110 2.98982
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.574852 1.124501 1.400 0.162451
currentDF[, TRAIT] 0.228683 0.059380 3.851 0.000145 ***
Age -0.007155 0.007436 -0.962 0.336724
Gendermale 0.150380 0.127961 1.175 0.240891
ORdate_year2003 -0.284168 0.182532 -1.557 0.120620
ORdate_year2004 -0.951344 0.171516 -5.547 6.63e-08 ***
ORdate_year2005 -1.283793 0.201489 -6.372 7.46e-10 ***
ORdate_year2006 -0.439442 0.588704 -0.746 0.456006
Hypertension.compositeyes -0.111328 0.178502 -0.624 0.533337
DiabetesStatusDiabetes 0.142079 0.146737 0.968 0.333733
SmokerStatusEx-smoker -0.059495 0.126436 -0.471 0.638317
SmokerStatusNever smoked 0.329693 0.211753 1.557 0.120583
Med.Statin.LLDyes -0.242302 0.126197 -1.920 0.055849 .
Med.all.antiplateletyes -0.006207 0.202918 -0.031 0.975618
GFR_MDRD -0.003865 0.003241 -1.193 0.233999
BMI -0.012399 0.015845 -0.783 0.434559
MedHx_CVDyes 0.118509 0.119155 0.995 0.320781
stenose50-70% -0.493868 0.769503 -0.642 0.521517
stenose70-90% 0.133629 0.710559 0.188 0.850962
stenose90-99% -0.004824 0.709504 -0.007 0.994580
stenose100% (Occlusion) -0.084794 0.917431 -0.092 0.926425
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9693 on 286 degrees of freedom
Multiple R-squared: 0.2426, Adjusted R-squared: 0.1896
F-statistic: 4.579 on 20 and 286 DF, p-value: 1.878e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL8_rank
Effect size...............: 0.228683
Standard error............: 0.05938
Odds ratio (effect size)..: 1.257
Lower 95% CI..............: 1.119
Upper 95% CI..............: 1.412
T-value...................: 3.851152
P-value...................: 0.0001451227
R^2.......................: 0.242559
Adjusted r^2..............: 0.189592
Sample size of AE DB......: 2423
Sample size of model......: 307
Missing data %............: 87.32976
- processing IL9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
DiabetesStatus + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 DiabetesStatusDiabetes Med.Statin.LLDyes
0.48828 0.14138 -0.08442 -0.88446 -0.95228 -0.48778 0.26200 -0.27541
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.69340 -0.76937 -0.08466 0.52110 2.90149
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.8102276 1.1091759 1.632 0.1036
currentDF[, TRAIT] 0.1288929 0.0578885 2.227 0.0267 *
Age -0.0070991 0.0073204 -0.970 0.3329
Gendermale 0.1565508 0.1258665 1.244 0.2145
ORdate_year2003 -0.1198702 0.2004486 -0.598 0.5502
ORdate_year2004 -0.9027959 0.1823040 -4.952 1.18e-06 ***
ORdate_year2005 -0.9788258 0.1866719 -5.244 2.84e-07 ***
ORdate_year2006 -0.5629846 0.3167032 -1.778 0.0764 .
Hypertension.compositeyes -0.1512993 0.1742371 -0.868 0.3858
DiabetesStatusDiabetes 0.2894576 0.1485375 1.949 0.0522 .
SmokerStatusEx-smoker -0.0413629 0.1284730 -0.322 0.7477
SmokerStatusNever smoked 0.2889767 0.1914196 1.510 0.1321
Med.Statin.LLDyes -0.2909307 0.1326474 -2.193 0.0290 *
Med.all.antiplateletyes -0.1154407 0.2107264 -0.548 0.5842
GFR_MDRD -0.0029002 0.0031826 -0.911 0.3628
BMI -0.0225756 0.0154863 -1.458 0.1459
MedHx_CVDyes 0.1236571 0.1198613 1.032 0.3030
stenose50-70% -0.3054376 0.8062192 -0.379 0.7050
stenose70-90% 0.0936706 0.7514915 0.125 0.9009
stenose90-99% 0.0001116 0.7483940 0.000 0.9999
stenose100% (Occlusion) -0.6459878 0.9292270 -0.695 0.4874
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.033 on 325 degrees of freedom
Multiple R-squared: 0.1963, Adjusted R-squared: 0.1469
F-statistic: 3.97 on 20 and 325 DF, p-value: 5.938e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL9_rank
Effect size...............: 0.128893
Standard error............: 0.057888
Odds ratio (effect size)..: 1.138
Lower 95% CI..............: 1.016
Upper 95% CI..............: 1.274
T-value...................: 2.226571
P-value...................: 0.02666097
R^2.......................: 0.196348
Adjusted r^2..............: 0.146893
Sample size of AE DB......: 2423
Sample size of model......: 346
Missing data %............: 85.72018
- processing IL10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year + SmokerStatus +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 SmokerStatusEx-smoker SmokerStatusNever smoked
0.33709 0.22064 -0.03833 -0.80238 -0.29174 -0.19439 0.40379
Med.Statin.LLDyes
-0.25344
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.63838 -0.70165 -0.08193 0.52078 2.79722
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.2905962 0.9344114 0.311 0.7561
currentDF[, TRAIT] 0.0095505 0.0655021 0.146 0.8842
Age 0.0038012 0.0077485 0.491 0.6241
Gendermale 0.2187829 0.1340714 1.632 0.1039
ORdate_year2003 -0.0383301 0.1846649 -0.208 0.8357
ORdate_year2004 -0.7780182 0.1769541 -4.397 1.61e-05 ***
ORdate_year2005 -0.3004605 0.2627038 -1.144 0.2538
Hypertension.compositeyes -0.1997055 0.1874088 -1.066 0.2876
DiabetesStatusDiabetes -0.0044378 0.1543338 -0.029 0.9771
SmokerStatusEx-smoker -0.1956120 0.1331980 -1.469 0.1432
SmokerStatusNever smoked 0.4453896 0.2175582 2.047 0.0417 *
Med.Statin.LLDyes -0.2387781 0.1355027 -1.762 0.0792 .
Med.all.antiplateletyes -0.2163693 0.2290059 -0.945 0.3456
GFR_MDRD -0.0005585 0.0036646 -0.152 0.8790
BMI -0.0038806 0.0176449 -0.220 0.8261
MedHx_CVDyes 0.0983735 0.1297711 0.758 0.4491
stenose70-90% 0.2470390 0.3553720 0.695 0.4876
stenose90-99% 0.2329505 0.3505326 0.665 0.5069
stenose100% (Occlusion) -0.5871020 0.6987346 -0.840 0.4016
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9666 on 256 degrees of freedom
Multiple R-squared: 0.1982, Adjusted R-squared: 0.1418
F-statistic: 3.516 on 18 and 256 DF, p-value: 3.876e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL10_rank
Effect size...............: 0.009551
Standard error............: 0.065502
Odds ratio (effect size)..: 1.01
Lower 95% CI..............: 0.888
Upper 95% CI..............: 1.148
T-value...................: 0.145805
P-value...................: 0.8841903
R^2.......................: 0.198205
Adjusted r^2..............: 0.141829
Sample size of AE DB......: 2423
Sample size of model......: 275
Missing data %............: 88.65043
- processing IL12_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year + Hypertension.composite +
SmokerStatus + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 Hypertension.compositeyes SmokerStatusEx-smoker
0.53494 0.18159 -0.08102 -0.81396 -0.64206 -0.25954 -0.13213
SmokerStatusNever smoked Med.Statin.LLDyes
0.41698 -0.18531
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.57930 -0.69401 -0.07406 0.52285 2.83752
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.0751604 0.9321075 -0.081 0.9358
currentDF[, TRAIT] 0.0553538 0.0636632 0.869 0.3854
Age 0.0061699 0.0077015 0.801 0.4238
Gendermale 0.1828476 0.1299281 1.407 0.1605
ORdate_year2003 0.0008249 0.1863804 0.004 0.9965
ORdate_year2004 -0.7333947 0.1743707 -4.206 3.55e-05 ***
ORdate_year2005 -0.5795305 0.2402889 -2.412 0.0165 *
Hypertension.compositeyes -0.2959402 0.1833444 -1.614 0.1077
DiabetesStatusDiabetes 0.0554171 0.1497363 0.370 0.7116
SmokerStatusEx-smoker -0.1640269 0.1331113 -1.232 0.2189
SmokerStatusNever smoked 0.4215065 0.2082262 2.024 0.0439 *
Med.Statin.LLDyes -0.1807940 0.1329366 -1.360 0.1750
Med.all.antiplateletyes -0.1448334 0.2224452 -0.651 0.5155
GFR_MDRD -0.0004322 0.0035500 -0.122 0.9032
BMI -0.0073649 0.0176517 -0.417 0.6768
MedHx_CVDyes 0.1468281 0.1248119 1.176 0.2405
stenose70-90% 0.4907985 0.3330821 1.474 0.1418
stenose90-99% 0.4211277 0.3282834 1.283 0.2007
stenose100% (Occlusion) 0.1013753 0.7947194 0.128 0.8986
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9608 on 268 degrees of freedom
Multiple R-squared: 0.1899, Adjusted R-squared: 0.1355
F-statistic: 3.489 on 18 and 268 DF, p-value: 4.174e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL12_rank
Effect size...............: 0.055354
Standard error............: 0.063663
Odds ratio (effect size)..: 1.057
Lower 95% CI..............: 0.933
Upper 95% CI..............: 1.197
T-value...................: 0.869479
P-value...................: 0.3853633
R^2.......................: 0.189866
Adjusted r^2..............: 0.135454
Sample size of AE DB......: 2423
Sample size of model......: 287
Missing data %............: 88.15518
- processing IL13_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes
0.5725 0.1782 -0.2667 -0.9420 -1.0452 -0.5322 -0.2217
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.76141 -0.70470 -0.08326 0.51894 2.88073
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.722592 1.042537 1.652 0.09932 .
currentDF[, TRAIT] 0.162891 0.054275 3.001 0.00287 **
Age -0.005644 0.006598 -0.855 0.39287
Gendermale 0.105143 0.113903 0.923 0.35656
ORdate_year2003 -0.281995 0.184166 -1.531 0.12657
ORdate_year2004 -0.943647 0.175629 -5.373 1.37e-07 ***
ORdate_year2005 -1.074766 0.182496 -5.889 8.68e-09 ***
ORdate_year2006 -0.596854 0.305508 -1.954 0.05149 .
Hypertension.compositeyes -0.037693 0.155561 -0.242 0.80868
DiabetesStatusDiabetes 0.153037 0.130143 1.176 0.24038
SmokerStatusEx-smoker -0.040902 0.115830 -0.353 0.72420
SmokerStatusNever smoked 0.276200 0.178380 1.548 0.12238
Med.Statin.LLDyes -0.250835 0.120085 -2.089 0.03740 *
Med.all.antiplateletyes -0.173681 0.182107 -0.954 0.34084
GFR_MDRD -0.002164 0.002947 -0.734 0.46316
BMI -0.020197 0.014082 -1.434 0.15234
MedHx_CVDyes 0.100478 0.107994 0.930 0.35277
stenose50-70% -0.386836 0.775811 -0.499 0.61834
stenose70-90% 0.055060 0.728774 0.076 0.93982
stenose90-99% -0.068196 0.726977 -0.094 0.92531
stenose100% (Occlusion) -0.707558 0.897120 -0.789 0.43079
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.003 on 372 degrees of freedom
Multiple R-squared: 0.1876, Adjusted R-squared: 0.1439
F-statistic: 4.294 on 20 and 372 DF, p-value: 5.588e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL13_rank
Effect size...............: 0.162891
Standard error............: 0.054275
Odds ratio (effect size)..: 1.177
Lower 95% CI..............: 1.058
Upper 95% CI..............: 1.309
T-value...................: 3.001209
P-value...................: 0.002870497
R^2.......................: 0.187555
Adjusted r^2..............: 0.143876
Sample size of AE DB......: 2423
Sample size of model......: 393
Missing data %............: 83.78044
- processing IL21_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes
0.5602 0.1734 -0.2606 -0.9333 -1.0281 -0.5105 -0.2179
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.74472 -0.71323 -0.04641 0.49905 2.89440
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.780480 1.042882 1.707 0.08860 .
currentDF[, TRAIT] 0.155569 0.054985 2.829 0.00492 **
Age -0.006175 0.006565 -0.941 0.34749
Gendermale 0.100250 0.113820 0.881 0.37900
ORdate_year2003 -0.274167 0.183452 -1.494 0.13589
ORdate_year2004 -0.932796 0.175298 -5.321 1.78e-07 ***
ORdate_year2005 -1.056501 0.181810 -5.811 1.33e-08 ***
ORdate_year2006 -0.575310 0.305741 -1.882 0.06066 .
Hypertension.compositeyes -0.048920 0.155355 -0.315 0.75302
DiabetesStatusDiabetes 0.141286 0.129932 1.087 0.27757
SmokerStatusEx-smoker -0.035457 0.115546 -0.307 0.75912
SmokerStatusNever smoked 0.280882 0.178367 1.575 0.11616
Med.Statin.LLDyes -0.251159 0.119835 -2.096 0.03677 *
Med.all.antiplateletyes -0.169581 0.182074 -0.931 0.35226
GFR_MDRD -0.002220 0.002947 -0.753 0.45172
BMI -0.020806 0.014076 -1.478 0.14022
MedHx_CVDyes 0.105471 0.107716 0.979 0.32814
stenose50-70% -0.403971 0.776561 -0.520 0.60323
stenose70-90% 0.049451 0.729376 0.068 0.94598
stenose90-99% -0.074406 0.727676 -0.102 0.91861
stenose100% (Occlusion) -0.751619 0.897644 -0.837 0.40295
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.003 on 373 degrees of freedom
Multiple R-squared: 0.1852, Adjusted R-squared: 0.1415
F-statistic: 4.239 on 20 and 373 DF, p-value: 7.93e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL21_rank
Effect size...............: 0.155569
Standard error............: 0.054985
Odds ratio (effect size)..: 1.168
Lower 95% CI..............: 1.049
Upper 95% CI..............: 1.301
T-value...................: 2.829327
P-value...................: 0.004916738
R^2.......................: 0.185195
Adjusted r^2..............: 0.141505
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing INFG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year + SmokerStatus +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 SmokerStatusEx-smoker
0.3637 0.2289 -0.1277 -0.8884 -0.9027 -1.1385 -0.1107
SmokerStatusNever smoked Med.Statin.LLDyes
0.3440 -0.2217
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.63858 -0.69644 -0.07796 0.47717 2.85622
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.851372 1.131368 0.753 0.452362
currentDF[, TRAIT] 0.038979 0.068294 0.571 0.568620
Age -0.001673 0.007545 -0.222 0.824686
Gendermale 0.250001 0.130240 1.920 0.055913 .
ORdate_year2003 -0.121209 0.183393 -0.661 0.509196
ORdate_year2004 -0.852312 0.174351 -4.888 1.7e-06 ***
ORdate_year2005 -0.876243 0.224812 -3.898 0.000121 ***
ORdate_year2006 -1.271176 1.032604 -1.231 0.219323
Hypertension.compositeyes -0.228672 0.184091 -1.242 0.215195
DiabetesStatusDiabetes 0.043448 0.147395 0.295 0.768383
SmokerStatusEx-smoker -0.103727 0.129968 -0.798 0.425478
SmokerStatusNever smoked 0.413651 0.210344 1.967 0.050205 .
Med.Statin.LLDyes -0.217198 0.134334 -1.617 0.107018
Med.all.antiplateletyes -0.198811 0.205735 -0.966 0.334693
GFR_MDRD -0.001441 0.003378 -0.427 0.669922
BMI -0.009351 0.016005 -0.584 0.559514
MedHx_CVDyes 0.116798 0.125085 0.934 0.351222
stenose50-70% -0.179490 0.802156 -0.224 0.823104
stenose70-90% 0.300988 0.733562 0.410 0.681887
stenose90-99% 0.202014 0.730792 0.276 0.782417
stenose100% (Occlusion) -0.132248 1.054680 -0.125 0.900302
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9903 on 285 degrees of freedom
Multiple R-squared: 0.1963, Adjusted R-squared: 0.1399
F-statistic: 3.48 on 20 and 285 DF, p-value: 1.572e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: INFG_rank
Effect size...............: 0.038979
Standard error............: 0.068294
Odds ratio (effect size)..: 1.04
Lower 95% CI..............: 0.909
Upper 95% CI..............: 1.189
T-value...................: 0.570748
P-value...................: 0.5686202
R^2.......................: 0.196264
Adjusted r^2..............: 0.139861
Sample size of AE DB......: 2423
Sample size of model......: 306
Missing data %............: 87.37103
- processing TNFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year + Med.Statin.LLD,
data = currentDF)
Coefficients:
(Intercept) ORdate_year2003 ORdate_year2004 ORdate_year2005 Med.Statin.LLDyes
0.44481 -0.09529 -0.82628 -0.75169 -0.21008
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.72939 -0.65759 -0.06138 0.54275 2.77535
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0648327 0.9110613 0.071 0.94332
currentDF[, TRAIT] 0.0283568 0.0641477 0.442 0.65882
Age 0.0023729 0.0075799 0.313 0.75450
Gendermale 0.1764500 0.1303642 1.354 0.17707
ORdate_year2003 -0.0698361 0.1885300 -0.370 0.71137
ORdate_year2004 -0.7708772 0.1835629 -4.200 3.68e-05 ***
ORdate_year2005 -0.7656403 0.2347234 -3.262 0.00125 **
Hypertension.compositeyes -0.2170170 0.1809287 -1.199 0.23144
DiabetesStatusDiabetes -0.1048371 0.1491450 -0.703 0.48273
SmokerStatusEx-smoker -0.0874939 0.1323019 -0.661 0.50899
SmokerStatusNever smoked 0.3729099 0.2117298 1.761 0.07937 .
Med.Statin.LLDyes -0.1990821 0.1318797 -1.510 0.13237
Med.all.antiplateletyes -0.0924533 0.2254802 -0.410 0.68212
GFR_MDRD 0.0000391 0.0035131 0.011 0.99113
BMI -0.0053743 0.0177154 -0.303 0.76185
MedHx_CVDyes 0.1303118 0.1259050 1.035 0.30163
stenose70-90% 0.4740739 0.3323083 1.427 0.15489
stenose90-99% 0.4150146 0.3273037 1.268 0.20594
stenose100% (Occlusion) 0.1304452 0.7915810 0.165 0.86924
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9509 on 260 degrees of freedom
Multiple R-squared: 0.1857, Adjusted R-squared: 0.1293
F-statistic: 3.294 on 18 and 260 DF, p-value: 1.273e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: TNFA_rank
Effect size...............: 0.028357
Standard error............: 0.064148
Odds ratio (effect size)..: 1.029
Lower 95% CI..............: 0.907
Upper 95% CI..............: 1.167
T-value...................: 0.442055
P-value...................: 0.6588167
R^2.......................: 0.185702
Adjusted r^2..............: 0.129328
Sample size of AE DB......: 2423
Sample size of model......: 279
Missing data %............: 88.48535
- processing MIF_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year + Med.Statin.LLD,
data = currentDF)
Coefficients:
(Intercept) ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes
0.4907 -0.1792 -0.8373 -0.9546 -0.5377 -0.2120
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.6993 -0.7283 -0.0810 0.5001 2.9626
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.680128 1.052862 1.596 0.1114
currentDF[, TRAIT] 0.046949 0.061646 0.762 0.4468
Age -0.008214 0.006595 -1.245 0.2138
Gendermale 0.129088 0.114716 1.125 0.2612
ORdate_year2003 -0.208715 0.183733 -1.136 0.2567
ORdate_year2004 -0.807068 0.182864 -4.413 1.33e-05 ***
ORdate_year2005 -0.949932 0.188950 -5.027 7.73e-07 ***
ORdate_year2006 -0.555638 0.316398 -1.756 0.0799 .
Hypertension.compositeyes -0.059883 0.156913 -0.382 0.7030
DiabetesStatusDiabetes 0.141402 0.132437 1.068 0.2864
SmokerStatusEx-smoker -0.002352 0.116840 -0.020 0.9839
SmokerStatusNever smoked 0.349230 0.178971 1.951 0.0518 .
Med.Statin.LLDyes -0.251522 0.121048 -2.078 0.0384 *
Med.all.antiplateletyes -0.160095 0.184344 -0.868 0.3857
GFR_MDRD -0.002345 0.003004 -0.781 0.4355
BMI -0.022242 0.014241 -1.562 0.1192
MedHx_CVDyes 0.107051 0.109025 0.982 0.3268
stenose50-70% -0.270619 0.783302 -0.345 0.7299
stenose70-90% 0.201540 0.735087 0.274 0.7841
stenose90-99% 0.087317 0.733428 0.119 0.9053
stenose100% (Occlusion) -0.688574 0.906695 -0.759 0.4481
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.013 on 373 degrees of freedom
Multiple R-squared: 0.169, Adjusted R-squared: 0.1244
F-statistic: 3.793 on 20 and 373 DF, p-value: 1.408e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MIF_rank
Effect size...............: 0.046949
Standard error............: 0.061646
Odds ratio (effect size)..: 1.048
Lower 95% CI..............: 0.929
Upper 95% CI..............: 1.183
T-value...................: 0.761587
P-value...................: 0.4467877
R^2.......................: 0.169
Adjusted r^2..............: 0.124442
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing MCP1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes
0.3838 0.2201 -0.1526 -0.7192 -0.8292 -0.3399 -0.1938
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.96517 -0.70768 -0.07711 0.50094 2.88155
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.299829 1.040748 1.249 0.2125
currentDF[, TRAIT] 0.204449 0.051886 3.940 9.73e-05 ***
Age -0.007225 0.006549 -1.103 0.2706
Gendermale 0.093016 0.113954 0.816 0.4149
ORdate_year2003 -0.176593 0.181410 -0.973 0.3310
ORdate_year2004 -0.736107 0.173401 -4.245 2.77e-05 ***
ORdate_year2005 -0.866819 0.180958 -4.790 2.42e-06 ***
ORdate_year2006 -0.415840 0.306640 -1.356 0.1759
Hypertension.compositeyes -0.030602 0.154593 -0.198 0.8432
DiabetesStatusDiabetes 0.181716 0.130368 1.394 0.1642
SmokerStatusEx-smoker -0.008339 0.114845 -0.073 0.9422
SmokerStatusNever smoked 0.304681 0.176004 1.731 0.0843 .
Med.Statin.LLDyes -0.227400 0.119381 -1.905 0.0576 .
Med.all.antiplateletyes -0.132302 0.185679 -0.713 0.4766
GFR_MDRD -0.002579 0.002928 -0.881 0.3790
BMI -0.016896 0.014069 -1.201 0.2306
MedHx_CVDyes 0.090760 0.107615 0.843 0.3996
stenose50-70% -0.176470 0.767277 -0.230 0.8182
stenose70-90% 0.273598 0.719635 0.380 0.7040
stenose90-99% 0.190775 0.717461 0.266 0.7905
stenose100% (Occlusion) -0.362846 0.891843 -0.407 0.6844
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9936 on 369 degrees of freedom
Multiple R-squared: 0.1993, Adjusted R-squared: 0.1559
F-statistic: 4.593 on 20 and 369 DF, p-value: 8.219e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MCP1_rank
Effect size...............: 0.204449
Standard error............: 0.051886
Odds ratio (effect size)..: 1.227
Lower 95% CI..............: 1.108
Upper 95% CI..............: 1.358
T-value...................: 3.940347
P-value...................: 9.731753e-05
R^2.......................: 0.199309
Adjusted r^2..............: 0.155911
Sample size of AE DB......: 2423
Sample size of model......: 390
Missing data %............: 83.90425
- processing MIP1a_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
DiabetesStatus + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 DiabetesStatusDiabetes Med.Statin.LLDyes
0.5181 0.1929 -0.1537 -0.9223 -0.9807 -0.4557 0.2157 -0.2850
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.69866 -0.73871 -0.07332 0.50985 2.96138
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.938879 1.085113 1.787 0.07487 .
currentDF[, TRAIT] 0.181772 0.058002 3.134 0.00188 **
Age -0.007201 0.007114 -1.012 0.31218
Gendermale 0.117253 0.123533 0.949 0.34322
ORdate_year2003 -0.192728 0.193593 -0.996 0.32019
ORdate_year2004 -0.936920 0.180287 -5.197 3.53e-07 ***
ORdate_year2005 -1.006058 0.184628 -5.449 9.81e-08 ***
ORdate_year2006 -0.515378 0.313164 -1.646 0.10076
Hypertension.compositeyes -0.135438 0.167816 -0.807 0.42020
DiabetesStatusDiabetes 0.233291 0.140881 1.656 0.09867 .
SmokerStatusEx-smoker -0.056423 0.124841 -0.452 0.65159
SmokerStatusNever smoked 0.258104 0.187207 1.379 0.16890
Med.Statin.LLDyes -0.301353 0.129154 -2.333 0.02022 *
Med.all.antiplateletyes -0.138251 0.204422 -0.676 0.49932
GFR_MDRD -0.002402 0.003079 -0.780 0.43581
BMI -0.020301 0.014892 -1.363 0.17374
MedHx_CVDyes 0.110613 0.116709 0.948 0.34393
stenose50-70% -0.423963 0.799266 -0.530 0.59616
stenose70-90% -0.036792 0.745454 -0.049 0.96067
stenose90-99% -0.137485 0.743131 -0.185 0.85333
stenose100% (Occlusion) -0.772043 0.919442 -0.840 0.40168
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.022 on 336 degrees of freedom
Multiple R-squared: 0.1996, Adjusted R-squared: 0.152
F-statistic: 4.19 on 20 and 336 DF, p-value: 1.375e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.181772
Standard error............: 0.058002
Odds ratio (effect size)..: 1.199
Lower 95% CI..............: 1.07
Upper 95% CI..............: 1.344
T-value...................: 3.13386
P-value...................: 0.001877172
R^2.......................: 0.199627
Adjusted r^2..............: 0.151985
Sample size of AE DB......: 2423
Sample size of model......: 357
Missing data %............: 85.2662
- processing RANTES_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year + Med.Statin.LLD,
data = currentDF)
Coefficients:
(Intercept) ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes
0.5043 -0.1838 -0.8282 -0.9519 -0.5300 -0.2334
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7262 -0.7430 -0.0855 0.5231 2.9569
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.730494 1.064620 1.625 0.1049
currentDF[, TRAIT] 0.008130 0.059408 0.137 0.8912
Age -0.008959 0.006709 -1.335 0.1826
Gendermale 0.130437 0.117319 1.112 0.2669
ORdate_year2003 -0.216998 0.191028 -1.136 0.2567
ORdate_year2004 -0.831820 0.193734 -4.294 2.25e-05 ***
ORdate_year2005 -0.975780 0.195300 -4.996 9.07e-07 ***
ORdate_year2006 -0.596100 0.317826 -1.876 0.0615 .
Hypertension.compositeyes -0.091521 0.160298 -0.571 0.5684
DiabetesStatusDiabetes 0.144676 0.135147 1.071 0.2851
SmokerStatusEx-smoker 0.005664 0.117991 0.048 0.9617
SmokerStatusNever smoked 0.353664 0.180342 1.961 0.0506 .
Med.Statin.LLDyes -0.269661 0.122627 -2.199 0.0285 *
Med.all.antiplateletyes -0.127879 0.190788 -0.670 0.5031
GFR_MDRD -0.003027 0.003008 -1.006 0.3151
BMI -0.020908 0.014417 -1.450 0.1478
MedHx_CVDyes 0.114884 0.110980 1.035 0.3013
stenose50-70% -0.267149 0.790312 -0.338 0.7355
stenose70-90% 0.219543 0.737923 0.298 0.7662
stenose90-99% 0.129344 0.735217 0.176 0.8604
stenose100% (Occlusion) -0.664077 0.913809 -0.727 0.4679
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.018 on 366 degrees of freedom
Multiple R-squared: 0.1662, Adjusted R-squared: 0.1206
F-statistic: 3.647 on 20 and 366 DF, p-value: 3.682e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: RANTES_rank
Effect size...............: 0.00813
Standard error............: 0.059408
Odds ratio (effect size)..: 1.008
Lower 95% CI..............: 0.897
Upper 95% CI..............: 1.133
T-value...................: 0.136851
P-value...................: 0.891224
R^2.......................: 0.166181
Adjusted r^2..............: 0.120617
Sample size of AE DB......: 2423
Sample size of model......: 387
Missing data %............: 84.02806
- processing MIG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes
0.5787 0.1479 -0.1903 -0.9450 -1.0577 -0.6136 -0.2347
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7582 -0.7231 -0.1288 0.5131 2.9314
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.903214 1.062462 1.791 0.0741 .
currentDF[, TRAIT] 0.134381 0.058291 2.305 0.0217 *
Age -0.007099 0.006812 -1.042 0.2980
Gendermale 0.095824 0.116675 0.821 0.4120
ORdate_year2003 -0.220460 0.185353 -1.189 0.2351
ORdate_year2004 -0.950210 0.180844 -5.254 2.54e-07 ***
ORdate_year2005 -1.080077 0.187677 -5.755 1.85e-08 ***
ORdate_year2006 -0.675508 0.310581 -2.175 0.0303 *
Hypertension.compositeyes -0.069152 0.159592 -0.433 0.6651
DiabetesStatusDiabetes 0.174465 0.134213 1.300 0.1945
SmokerStatusEx-smoker -0.038212 0.118686 -0.322 0.7477
SmokerStatusNever smoked 0.233249 0.184215 1.266 0.2063
Med.Statin.LLDyes -0.270750 0.122713 -2.206 0.0280 *
Med.all.antiplateletyes -0.199282 0.187343 -1.064 0.2882
GFR_MDRD -0.002729 0.002998 -0.910 0.3632
BMI -0.019773 0.014343 -1.379 0.1689
MedHx_CVDyes 0.082975 0.111227 0.746 0.4562
stenose50-70% -0.403205 0.788862 -0.511 0.6096
stenose70-90% 0.074402 0.737564 0.101 0.9197
stenose90-99% -0.033596 0.735567 -0.046 0.9636
stenose100% (Occlusion) -0.782111 0.909254 -0.860 0.3903
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.014 on 362 degrees of freedom
Multiple R-squared: 0.1787, Adjusted R-squared: 0.1334
F-statistic: 3.939 on 20 and 362 DF, p-value: 5.824e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MIG_rank
Effect size...............: 0.134381
Standard error............: 0.058291
Odds ratio (effect size)..: 1.144
Lower 95% CI..............: 1.02
Upper 95% CI..............: 1.282
T-value...................: 2.30534
P-value...................: 0.0217124
R^2.......................: 0.17873
Adjusted r^2..............: 0.133356
Sample size of AE DB......: 2423
Sample size of model......: 383
Missing data %............: 84.19315
- processing IP10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
DiabetesStatus + Med.Statin.LLD + MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 DiabetesStatusDiabetes Med.Statin.LLDyes
0.3802 0.1763 -0.1781 -0.8910 -0.9479 -0.5493 0.2232 -0.2870
MedHx_CVDyes
0.1828
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.64753 -0.69308 -0.07575 0.55971 2.83321
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.494848 1.084296 1.379 0.16894
currentDF[, TRAIT] 0.170110 0.056428 3.015 0.00277 **
Age -0.004673 0.007118 -0.657 0.51192
Gendermale 0.134742 0.120729 1.116 0.26520
ORdate_year2003 -0.203479 0.190220 -1.070 0.28554
ORdate_year2004 -0.904104 0.177001 -5.108 5.53e-07 ***
ORdate_year2005 -0.952862 0.182600 -5.218 3.20e-07 ***
ORdate_year2006 -0.616887 0.325492 -1.895 0.05894 .
Hypertension.compositeyes -0.120591 0.165613 -0.728 0.46704
DiabetesStatusDiabetes 0.222226 0.140773 1.579 0.11539
SmokerStatusEx-smoker -0.064378 0.124466 -0.517 0.60534
SmokerStatusNever smoked 0.121339 0.193670 0.627 0.53141
Med.Statin.LLDyes -0.301178 0.127165 -2.368 0.01844 *
Med.all.antiplateletyes -0.173854 0.191737 -0.907 0.36521
GFR_MDRD -0.003545 0.003106 -1.141 0.25462
BMI -0.015537 0.014891 -1.043 0.29752
MedHx_CVDyes 0.193807 0.115193 1.682 0.09343 .
stenose50-70% -0.281187 0.781887 -0.360 0.71936
stenose70-90% 0.157402 0.731392 0.215 0.82974
stenose90-99% 0.031491 0.728418 0.043 0.96554
stenose100% (Occlusion) -0.501814 0.902643 -0.556 0.57863
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.006 on 329 degrees of freedom
Multiple R-squared: 0.2002, Adjusted R-squared: 0.1515
F-statistic: 4.117 on 20 and 329 DF, p-value: 2.295e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IP10_rank
Effect size...............: 0.17011
Standard error............: 0.056428
Odds ratio (effect size)..: 1.185
Lower 95% CI..............: 1.061
Upper 95% CI..............: 1.324
T-value...................: 3.014663
P-value...................: 0.00277265
R^2.......................: 0.200171
Adjusted r^2..............: 0.151549
Sample size of AE DB......: 2423
Sample size of model......: 350
Missing data %............: 85.5551
- processing Eotaxin1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes
0.5358 0.1412 -0.2025 -0.9222 -1.0161 -0.5359 -0.2101
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.79922 -0.72633 -0.08739 0.48454 2.92510
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.778813 1.048267 1.697 0.0905 .
currentDF[, TRAIT] 0.118353 0.056735 2.086 0.0377 *
Age -0.007062 0.006582 -1.073 0.2840
Gendermale 0.100448 0.114986 0.874 0.3829
ORdate_year2003 -0.223401 0.182965 -1.221 0.2229
ORdate_year2004 -0.918054 0.176746 -5.194 3.39e-07 ***
ORdate_year2005 -1.041671 0.182868 -5.696 2.49e-08 ***
ORdate_year2006 -0.598229 0.307028 -1.948 0.0521 .
Hypertension.compositeyes -0.055514 0.156067 -0.356 0.7223
DiabetesStatusDiabetes 0.133996 0.130504 1.027 0.3052
SmokerStatusEx-smoker -0.027858 0.116064 -0.240 0.8104
SmokerStatusNever smoked 0.293783 0.179419 1.637 0.1024
Med.Statin.LLDyes -0.245536 0.120427 -2.039 0.0422 *
Med.all.antiplateletyes -0.167073 0.183016 -0.913 0.3619
GFR_MDRD -0.002269 0.002963 -0.766 0.4443
BMI -0.020942 0.014145 -1.481 0.1396
MedHx_CVDyes 0.106228 0.108252 0.981 0.3271
stenose50-70% -0.361011 0.780339 -0.463 0.6439
stenose70-90% 0.092246 0.733174 0.126 0.8999
stenose90-99% -0.027717 0.731505 -0.038 0.9698
stenose100% (Occlusion) -0.749429 0.902408 -0.830 0.4068
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.008 on 373 degrees of freedom
Multiple R-squared: 0.1773, Adjusted R-squared: 0.1332
F-statistic: 4.019 on 20 and 373 DF, p-value: 3.277e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.118353
Standard error............: 0.056735
Odds ratio (effect size)..: 1.126
Lower 95% CI..............: 1.007
Upper 95% CI..............: 1.258
T-value...................: 2.08606
P-value...................: 0.03765211
R^2.......................: 0.177306
Adjusted r^2..............: 0.133194
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing TARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes
0.6434 0.1146 -0.3104 -0.9412 -1.0313 -0.6299 -0.2696
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.70514 -0.67487 -0.08392 0.48463 2.94524
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.275473 1.115634 2.040 0.042222 *
currentDF[, TRAIT] 0.095182 0.060142 1.583 0.114515
Age -0.009597 0.007062 -1.359 0.175153
Gendermale 0.112625 0.123514 0.912 0.362553
ORdate_year2003 -0.359253 0.293941 -1.222 0.222547
ORdate_year2004 -0.970165 0.288817 -3.359 0.000878 ***
ORdate_year2005 -1.068921 0.294339 -3.632 0.000329 ***
ORdate_year2006 -0.722020 0.383646 -1.882 0.060759 .
Hypertension.compositeyes -0.133751 0.166714 -0.802 0.422996
DiabetesStatusDiabetes 0.144731 0.142820 1.013 0.311659
SmokerStatusEx-smoker -0.002949 0.126213 -0.023 0.981373
SmokerStatusNever smoked 0.290877 0.186851 1.557 0.120538
Med.Statin.LLDyes -0.310170 0.133677 -2.320 0.020963 *
Med.all.antiplateletyes -0.103190 0.199212 -0.518 0.604828
GFR_MDRD -0.004520 0.003276 -1.380 0.168594
BMI -0.024855 0.015564 -1.597 0.111287
MedHx_CVDyes 0.056964 0.118736 0.480 0.631736
stenose50-70% -0.239195 0.786943 -0.304 0.761363
stenose70-90% 0.172702 0.732718 0.236 0.813818
stenose90-99% 0.057089 0.730707 0.078 0.937775
stenose100% (Occlusion) -0.715946 0.907820 -0.789 0.430914
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.008 on 315 degrees of freedom
Multiple R-squared: 0.174, Adjusted R-squared: 0.1215
F-statistic: 3.317 on 20 and 315 DF, p-value: 3.613e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: TARC_rank
Effect size...............: 0.095182
Standard error............: 0.060142
Odds ratio (effect size)..: 1.1
Lower 95% CI..............: 0.978
Upper 95% CI..............: 1.237
T-value...................: 1.582607
P-value...................: 0.1145146
R^2.......................: 0.173985
Adjusted r^2..............: 0.121539
Sample size of AE DB......: 2423
Sample size of model......: 336
Missing data %............: 86.13289
- processing PARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year + Med.Statin.LLD,
data = currentDF)
Coefficients:
(Intercept) ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes
0.4907 -0.1792 -0.8373 -0.9546 -0.5377 -0.2120
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.71989 -0.70730 -0.06164 0.53611 2.99389
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.582978 1.058690 1.495 0.1357
currentDF[, TRAIT] 0.055248 0.058467 0.945 0.3453
Age -0.008333 0.006576 -1.267 0.2059
Gendermale 0.135924 0.114135 1.191 0.2345
ORdate_year2003 -0.193724 0.184212 -1.052 0.2936
ORdate_year2004 -0.789544 0.185321 -4.260 2.59e-05 ***
ORdate_year2005 -0.943846 0.187988 -5.021 7.99e-07 ***
ORdate_year2006 -0.552955 0.314061 -1.761 0.0791 .
Hypertension.compositeyes -0.051805 0.157307 -0.329 0.7421
DiabetesStatusDiabetes 0.141310 0.131912 1.071 0.2848
SmokerStatusEx-smoker -0.009240 0.116308 -0.079 0.9367
SmokerStatusNever smoked 0.336778 0.178808 1.883 0.0604 .
Med.Statin.LLDyes -0.251248 0.120982 -2.077 0.0385 *
Med.all.antiplateletyes -0.135417 0.184170 -0.735 0.4626
GFR_MDRD -0.002432 0.002982 -0.815 0.4153
BMI -0.021537 0.014207 -1.516 0.1304
MedHx_CVDyes 0.109082 0.108780 1.003 0.3166
stenose50-70% -0.205673 0.782224 -0.263 0.7927
stenose70-90% 0.249242 0.733280 0.340 0.7341
stenose90-99% 0.137892 0.730953 0.189 0.8505
stenose100% (Occlusion) -0.587729 0.908806 -0.647 0.5182
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.013 on 373 degrees of freedom
Multiple R-squared: 0.1697, Adjusted R-squared: 0.1252
F-statistic: 3.812 on 20 and 373 DF, p-value: 1.248e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: PARC_rank
Effect size...............: 0.055248
Standard error............: 0.058467
Odds ratio (effect size)..: 1.057
Lower 95% CI..............: 0.942
Upper 95% CI..............: 1.185
T-value...................: 0.944933
P-value...................: 0.3453049
R^2.......................: 0.169695
Adjusted r^2..............: 0.125175
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing MDC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year + DiabetesStatus +
Med.Statin.LLD + BMI, data = currentDF)
Coefficients:
(Intercept) ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 DiabetesStatusDiabetes Med.Statin.LLDyes BMI
1.0660 -0.1306 -0.8854 -0.9787 -0.5573 0.2704 -0.2841 -0.0211
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.6682 -0.7517 -0.0666 0.5286 2.8390
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.017526 1.097071 1.839 0.0668 .
currentDF[, TRAIT] 0.049143 0.061012 0.805 0.4211
Age -0.010295 0.007075 -1.455 0.1466
Gendermale 0.151610 0.124352 1.219 0.2236
ORdate_year2003 -0.136186 0.199195 -0.684 0.4947
ORdate_year2004 -0.851408 0.188467 -4.518 8.70e-06 ***
ORdate_year2005 -0.942065 0.195478 -4.819 2.19e-06 ***
ORdate_year2006 -0.549932 0.325160 -1.691 0.0917 .
Hypertension.compositeyes -0.142185 0.170424 -0.834 0.4047
DiabetesStatusDiabetes 0.258436 0.144449 1.789 0.0745 .
SmokerStatusEx-smoker 0.010200 0.125640 0.081 0.9353
SmokerStatusNever smoked 0.328893 0.189214 1.738 0.0831 .
Med.Statin.LLDyes -0.309887 0.129911 -2.385 0.0176 *
Med.all.antiplateletyes -0.120409 0.210565 -0.572 0.5678
GFR_MDRD -0.003111 0.003148 -0.988 0.3237
BMI -0.024950 0.015108 -1.651 0.0996 .
MedHx_CVDyes 0.126197 0.118093 1.069 0.2860
stenose50-70% -0.267914 0.805407 -0.333 0.7396
stenose70-90% 0.140971 0.750838 0.188 0.8512
stenose90-99% 0.036436 0.748580 0.049 0.9612
stenose100% (Occlusion) -0.694186 0.927480 -0.748 0.4547
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.032 on 333 degrees of freedom
Multiple R-squared: 0.1886, Adjusted R-squared: 0.1399
F-statistic: 3.87 on 20 and 333 DF, p-value: 1.061e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MDC_rank
Effect size...............: 0.049143
Standard error............: 0.061012
Odds ratio (effect size)..: 1.05
Lower 95% CI..............: 0.932
Upper 95% CI..............: 1.184
T-value...................: 0.805466
P-value...................: 0.4211255
R^2.......................: 0.188602
Adjusted r^2..............: 0.139869
Sample size of AE DB......: 2423
Sample size of model......: 354
Missing data %............: 85.39001
- processing OPG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes
0.4541 0.1759 -0.1394 -0.7648 -0.9414 -0.5343 -0.2155
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.56004 -0.72629 -0.08267 0.50532 2.97681
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.502427 1.041544 1.443 0.15000
currentDF[, TRAIT] 0.170223 0.051788 3.287 0.00111 **
Age -0.006450 0.006544 -0.986 0.32496
Gendermale 0.096191 0.113815 0.845 0.39857
ORdate_year2003 -0.159682 0.182380 -0.876 0.38184
ORdate_year2004 -0.775990 0.173748 -4.466 1.06e-05 ***
ORdate_year2005 -0.973546 0.179835 -5.414 1.11e-07 ***
ORdate_year2006 -0.594052 0.304798 -1.949 0.05205 .
Hypertension.compositeyes -0.017145 0.155594 -0.110 0.91232
DiabetesStatusDiabetes 0.162778 0.130007 1.252 0.21133
SmokerStatusEx-smoker -0.037535 0.115396 -0.325 0.74516
SmokerStatusNever smoked 0.301494 0.177048 1.703 0.08942 .
Med.Statin.LLDyes -0.245153 0.119820 -2.046 0.04146 *
Med.all.antiplateletyes -0.189167 0.181909 -1.040 0.29906
GFR_MDRD -0.002569 0.002935 -0.875 0.38204
BMI -0.019096 0.014062 -1.358 0.17529
MedHx_CVDyes 0.107587 0.107691 0.999 0.31843
stenose50-70% -0.248591 0.772385 -0.322 0.74775
stenose70-90% 0.203190 0.724634 0.280 0.77932
stenose90-99% 0.086274 0.722408 0.119 0.90500
stenose100% (Occlusion) -0.494402 0.896347 -0.552 0.58157
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.001 on 372 degrees of freedom
Multiple R-squared: 0.1914, Adjusted R-squared: 0.1479
F-statistic: 4.402 on 20 and 372 DF, p-value: 2.775e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: OPG_rank
Effect size...............: 0.170223
Standard error............: 0.051788
Odds ratio (effect size)..: 1.186
Lower 95% CI..............: 1.071
Upper 95% CI..............: 1.312
T-value...................: 3.286933
P-value...................: 0.001109227
R^2.......................: 0.191369
Adjusted r^2..............: 0.147894
Sample size of AE DB......: 2423
Sample size of model......: 393
Missing data %............: 83.78044
- processing sICAM1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes
0.45999 0.08008 -0.18889 -0.79356 -0.91768 -0.45903 -0.20831
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.72217 -0.74290 -0.05725 0.50541 2.94304
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.631052 1.053147 1.549 0.1223
currentDF[, TRAIT] 0.061805 0.054509 1.134 0.2576
Age -0.007493 0.006643 -1.128 0.2601
Gendermale 0.131753 0.114178 1.154 0.2493
ORdate_year2003 -0.214109 0.183648 -1.166 0.2444
ORdate_year2004 -0.816299 0.176645 -4.621 5.27e-06 ***
ORdate_year2005 -0.964273 0.183090 -5.267 2.35e-07 ***
ORdate_year2006 -0.552143 0.312297 -1.768 0.0779 .
Hypertension.compositeyes -0.064175 0.156644 -0.410 0.6823
DiabetesStatusDiabetes 0.141312 0.131586 1.074 0.2836
SmokerStatusEx-smoker -0.010652 0.116239 -0.092 0.9270
SmokerStatusNever smoked 0.329674 0.178975 1.842 0.0663 .
Med.Statin.LLDyes -0.244354 0.120989 -2.020 0.0441 *
Med.all.antiplateletyes -0.146623 0.183556 -0.799 0.4249
GFR_MDRD -0.002363 0.002982 -0.793 0.4285
BMI -0.021974 0.014204 -1.547 0.1227
MedHx_CVDyes 0.101761 0.109094 0.933 0.3515
stenose50-70% -0.280886 0.782223 -0.359 0.7197
stenose70-90% 0.197621 0.733684 0.269 0.7878
stenose90-99% 0.084679 0.731653 0.116 0.9079
stenose100% (Occlusion) -0.654211 0.905096 -0.723 0.4703
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.012 on 373 degrees of freedom
Multiple R-squared: 0.1706, Adjusted R-squared: 0.1261
F-statistic: 3.835 on 20 and 373 DF, p-value: 1.072e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: sICAM1_rank
Effect size...............: 0.061805
Standard error............: 0.054509
Odds ratio (effect size)..: 1.064
Lower 95% CI..............: 0.956
Upper 95% CI..............: 1.184
T-value...................: 1.133852
P-value...................: 0.2575848
R^2.......................: 0.170567
Adjusted r^2..............: 0.126093
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing VEGFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
SmokerStatus + BMI + stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 SmokerStatusEx-smoker
0.3100 0.1488 -0.3608 -0.9321 -1.3793 -0.9478 -0.1011
SmokerStatusNever smoked BMI stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
0.4006 -0.0233 0.2856 0.9884 0.7546 0.1185
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.6193 -0.6751 -0.1247 0.4215 2.9367
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.266875 1.269215 0.998 0.31898
currentDF[, TRAIT] 0.128407 0.060387 2.126 0.03426 *
Age -0.006138 0.006893 -0.890 0.37390
Gendermale 0.165612 0.120365 1.376 0.16984
ORdate_year2003 -0.349780 0.208964 -1.674 0.09517 .
ORdate_year2004 -0.914493 0.194461 -4.703 3.88e-06 ***
ORdate_year2005 -1.304618 0.224342 -5.815 1.51e-08 ***
ORdate_year2006 -0.932227 0.340409 -2.739 0.00653 **
Hypertension.compositeyes -0.139406 0.168713 -0.826 0.40928
DiabetesStatusDiabetes 0.010712 0.135134 0.079 0.93687
SmokerStatusEx-smoker -0.079150 0.120710 -0.656 0.51250
SmokerStatusNever smoked 0.442442 0.191914 2.305 0.02181 *
Med.Statin.LLDyes -0.157909 0.124858 -1.265 0.20693
Med.all.antiplateletyes -0.197183 0.181708 -1.085 0.27869
GFR_MDRD -0.004177 0.002940 -1.421 0.15635
BMI -0.023773 0.015059 -1.579 0.11544
MedHx_CVDyes 0.029898 0.114860 0.260 0.79480
stenose50-70% 0.285074 1.019628 0.280 0.77998
stenose70-90% 0.975034 0.977541 0.997 0.31933
stenose90-99% 0.751462 0.976169 0.770 0.44200
stenose100% (Occlusion) -0.055368 1.100367 -0.050 0.95990
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9635 on 309 degrees of freedom
Multiple R-squared: 0.22, Adjusted R-squared: 0.1695
F-statistic: 4.357 on 20 and 309 DF, p-value: 5.937e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: VEGFA_rank
Effect size...............: 0.128407
Standard error............: 0.060387
Odds ratio (effect size)..: 1.137
Lower 95% CI..............: 1.01
Upper 95% CI..............: 1.28
T-value...................: 2.126402
P-value...................: 0.03426216
R^2.......................: 0.219972
Adjusted r^2..............: 0.169484
Sample size of AE DB......: 2423
Sample size of model......: 330
Missing data %............: 86.38052
- processing TGFB_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year + Med.Statin.LLD,
data = currentDF)
Coefficients:
(Intercept) ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes
0.4553 -0.1408 -0.8167 -0.9427 -0.3630 -0.1914
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7152 -0.7370 -0.0852 0.5230 2.9586
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.612565 1.069448 1.508 0.1325
currentDF[, TRAIT] 0.050009 0.054993 0.909 0.3638
Age -0.009335 0.006772 -1.379 0.1689
Gendermale 0.121694 0.117955 1.032 0.3029
ORdate_year2003 -0.147139 0.185496 -0.793 0.4282
ORdate_year2004 -0.774720 0.180851 -4.284 2.36e-05 ***
ORdate_year2005 -0.932096 0.185232 -5.032 7.65e-07 ***
ORdate_year2006 -0.428655 0.341954 -1.254 0.2108
Hypertension.compositeyes -0.061508 0.159534 -0.386 0.7001
DiabetesStatusDiabetes 0.114689 0.133280 0.861 0.3901
SmokerStatusEx-smoker 0.020781 0.118565 0.175 0.8610
SmokerStatusNever smoked 0.399602 0.184185 2.170 0.0307 *
Med.Statin.LLDyes -0.225852 0.123685 -1.826 0.0687 .
Med.all.antiplateletyes -0.129311 0.188424 -0.686 0.4930
GFR_MDRD -0.003083 0.003051 -1.011 0.3128
BMI -0.018662 0.014647 -1.274 0.2034
MedHx_CVDyes 0.110727 0.111240 0.995 0.3202
stenose50-70% -0.295659 0.786820 -0.376 0.7073
stenose70-90% 0.217406 0.740455 0.294 0.7692
stenose90-99% 0.115940 0.737987 0.157 0.8753
stenose100% (Occlusion) -0.670250 0.914891 -0.733 0.4643
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.021 on 363 degrees of freedom
Multiple R-squared: 0.1711, Adjusted R-squared: 0.1254
F-statistic: 3.745 on 20 and 363 DF, p-value: 1.999e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: TGFB_rank
Effect size...............: 0.050009
Standard error............: 0.054993
Odds ratio (effect size)..: 1.051
Lower 95% CI..............: 0.944
Upper 95% CI..............: 1.171
T-value...................: 0.909377
P-value...................: 0.3637543
R^2.......................: 0.171053
Adjusted r^2..............: 0.125381
Sample size of AE DB......: 2423
Sample size of model......: 384
Missing data %............: 84.15188
- processing MMP2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + SmokerStatus, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.17137 0.15534 0.20248 -0.17393 -0.81053 -1.01097 -0.41949
SmokerStatusEx-smoker SmokerStatusNever smoked
-0.05357 0.32333
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9387 -0.7245 -0.0918 0.5180 3.2271
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.533993 1.059548 1.448 0.1485
currentDF[, TRAIT] 0.141513 0.057312 2.469 0.0140 *
Age -0.008048 0.006581 -1.223 0.2222
Gendermale 0.206418 0.114801 1.798 0.0730 .
ORdate_year2003 -0.183477 0.178759 -1.026 0.3054
ORdate_year2004 -0.791539 0.171970 -4.603 5.75e-06 ***
ORdate_year2005 -0.975472 0.179534 -5.433 1.01e-07 ***
ORdate_year2006 -0.356392 0.608923 -0.585 0.5587
Hypertension.compositeyes -0.012672 0.161285 -0.079 0.9374
DiabetesStatusDiabetes 0.076327 0.130278 0.586 0.5583
SmokerStatusEx-smoker -0.005047 0.116297 -0.043 0.9654
SmokerStatusNever smoked 0.379249 0.179654 2.111 0.0354 *
Med.Statin.LLDyes -0.202984 0.119293 -1.702 0.0897 .
Med.all.antiplateletyes -0.211296 0.188063 -1.124 0.2619
GFR_MDRD -0.003392 0.002937 -1.155 0.2489
BMI -0.020504 0.014488 -1.415 0.1578
MedHx_CVDyes 0.112425 0.109318 1.028 0.3044
stenose50-70% -0.179621 0.774518 -0.232 0.8167
stenose70-90% 0.252456 0.730210 0.346 0.7297
stenose90-99% 0.169065 0.727684 0.232 0.8164
stenose100% (Occlusion) -0.670245 0.901957 -0.743 0.4579
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.007 on 368 degrees of freedom
Multiple R-squared: 0.1961, Adjusted R-squared: 0.1524
F-statistic: 4.489 on 20 and 368 DF, p-value: 1.621e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MMP2_rank
Effect size...............: 0.141513
Standard error............: 0.057312
Odds ratio (effect size)..: 1.152
Lower 95% CI..............: 1.03
Upper 95% CI..............: 1.289
T-value...................: 2.469185
P-value...................: 0.01399542
R^2.......................: 0.196117
Adjusted r^2..............: 0.152428
Sample size of AE DB......: 2423
Sample size of model......: 389
Missing data %............: 83.94552
- processing MMP8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
SmokerStatus + BMI, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 SmokerStatusEx-smoker
0.8495934 0.1801567 -0.1764966 -0.8563801 -1.0755297 -0.2790614 -0.0003182
SmokerStatusNever smoked BMI
0.3758202 -0.0201384
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.98547 -0.72780 -0.08382 0.57034 2.98688
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.209717 1.055418 2.094 0.0370 *
currentDF[, TRAIT] 0.167234 0.055783 2.998 0.0029 **
Age -0.009358 0.006521 -1.435 0.1521
Gendermale 0.108177 0.116397 0.929 0.3533
ORdate_year2003 -0.202115 0.178069 -1.135 0.2571
ORdate_year2004 -0.834075 0.169997 -4.906 1.40e-06 ***
ORdate_year2005 -1.052034 0.178579 -5.891 8.66e-09 ***
ORdate_year2006 -0.283643 0.607998 -0.467 0.6411
Hypertension.compositeyes -0.102895 0.158163 -0.651 0.5157
DiabetesStatusDiabetes 0.112274 0.130573 0.860 0.3904
SmokerStatusEx-smoker 0.037391 0.115761 0.323 0.7469
SmokerStatusNever smoked 0.456354 0.177700 2.568 0.0106 *
Med.Statin.LLDyes -0.198579 0.118861 -1.671 0.0956 .
Med.all.antiplateletyes -0.141898 0.187295 -0.758 0.4492
GFR_MDRD -0.002939 0.002937 -1.001 0.3177
BMI -0.028320 0.014553 -1.946 0.0524 .
MedHx_CVDyes 0.118998 0.108755 1.094 0.2746
stenose50-70% -0.541527 0.778227 -0.696 0.4870
stenose70-90% -0.103979 0.735610 -0.141 0.8877
stenose90-99% -0.147657 0.730957 -0.202 0.8400
stenose100% (Occlusion) -1.061519 0.907459 -1.170 0.2429
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.003 on 368 degrees of freedom
Multiple R-squared: 0.2023, Adjusted R-squared: 0.1589
F-statistic: 4.666 on 20 and 368 DF, p-value: 5.15e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MMP8_rank
Effect size...............: 0.167234
Standard error............: 0.055783
Odds ratio (effect size)..: 1.182
Lower 95% CI..............: 1.06
Upper 95% CI..............: 1.319
T-value...................: 2.997946
P-value...................: 0.002902687
R^2.......................: 0.202281
Adjusted r^2..............: 0.158927
Sample size of AE DB......: 2423
Sample size of model......: 389
Missing data %............: 83.94552
- processing MMP9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
SmokerStatus, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 SmokerStatusEx-smoker
0.30537 0.09345 -0.17621 -0.83204 -1.02703 -0.33467 -0.02021
SmokerStatusNever smoked
0.33418
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.86606 -0.74713 -0.07738 0.54148 3.00389
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.881170 1.057604 1.779 0.0761 .
currentDF[, TRAIT] 0.084928 0.053342 1.592 0.1122
Age -0.009613 0.006576 -1.462 0.1447
Gendermale 0.154242 0.116033 1.329 0.1846
ORdate_year2003 -0.193679 0.179584 -1.078 0.2815
ORdate_year2004 -0.818231 0.172239 -4.751 2.91e-06 ***
ORdate_year2005 -1.000118 0.179920 -5.559 5.23e-08 ***
ORdate_year2006 -0.339185 0.615132 -0.551 0.5817
Hypertension.compositeyes -0.066688 0.159797 -0.417 0.6767
DiabetesStatusDiabetes 0.087669 0.131443 0.667 0.5052
SmokerStatusEx-smoker 0.021922 0.116602 0.188 0.8510
SmokerStatusNever smoked 0.425521 0.179189 2.375 0.0181 *
Med.Statin.LLDyes -0.210606 0.119844 -1.757 0.0797 .
Med.all.antiplateletyes -0.180837 0.188510 -0.959 0.3380
GFR_MDRD -0.003352 0.002973 -1.127 0.2603
BMI -0.025096 0.014645 -1.714 0.0874 .
MedHx_CVDyes 0.127863 0.109655 1.166 0.2443
stenose50-70% -0.263834 0.778244 -0.339 0.7348
stenose70-90% 0.180903 0.734180 0.246 0.8055
stenose90-99% 0.090283 0.731640 0.123 0.9019
stenose100% (Occlusion) -0.747899 0.907281 -0.824 0.4103
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.012 on 368 degrees of freedom
Multiple R-squared: 0.1884, Adjusted R-squared: 0.1443
F-statistic: 4.271 on 20 and 368 DF, p-value: 6.643e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MMP9_rank
Effect size...............: 0.084928
Standard error............: 0.053342
Odds ratio (effect size)..: 1.089
Lower 95% CI..............: 0.981
Upper 95% CI..............: 1.209
T-value...................: 1.592137
P-value...................: 0.1122125
R^2.......................: 0.188389
Adjusted r^2..............: 0.14428
Sample size of AE DB......: 2423
Sample size of model......: 389
Missing data %............: 83.94552
Analysis of MCP1_pg_ml_2015_rank.
- processing IL2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + Med.all.antiplatelet + stenose,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-1.1190 -0.1028 0.1719 -0.2637 -0.7738 -0.4074 1.3776
Med.Statin.LLDyes Med.all.antiplateletyes stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.2191 -0.3144 1.2500 1.6185 1.4854 0.5953
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.70428 -0.55440 -0.00907 0.45652 2.66246
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.6509226 1.1698997 -1.411 0.1593
currentDF[, TRAIT] -0.0848934 0.0531063 -1.599 0.1110
Age 0.0074687 0.0064213 1.163 0.2457
Gendermale 0.2083025 0.1105631 1.884 0.0606 .
ORdate_year2003 -0.2477921 0.1582962 -1.565 0.1186
ORdate_year2004 -0.7519972 0.1508084 -4.986 1.06e-06 ***
ORdate_year2005 -0.4014415 0.1824173 -2.201 0.0285 *
ORdate_year2006 1.2846012 0.8978439 1.431 0.1536
Hypertension.compositeyes -0.0595028 0.1526610 -0.390 0.6970
DiabetesStatusDiabetes 0.0511650 0.1273001 0.402 0.6880
SmokerStatusEx-smoker -0.2188723 0.1128733 -1.939 0.0535 .
SmokerStatusNever smoked 0.0240996 0.1797577 0.134 0.8934
Med.Statin.LLDyes -0.1929294 0.1129256 -1.708 0.0886 .
Med.all.antiplateletyes -0.2886237 0.1865433 -1.547 0.1229
GFR_MDRD 0.0003329 0.0029001 0.115 0.9087
BMI 0.0017818 0.0151806 0.117 0.9066
MedHx_CVDyes 0.1236076 0.1057191 1.169 0.2433
stenose50-70% 1.1914842 0.9268343 1.286 0.1996
stenose70-90% 1.5833862 0.8793187 1.801 0.0728 .
stenose90-99% 1.4225580 0.8779798 1.620 0.1063
stenose100% (Occlusion) 0.5790383 1.0404448 0.557 0.5783
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8568 on 292 degrees of freedom
Multiple R-squared: 0.1938, Adjusted R-squared: 0.1386
F-statistic: 3.51 on 20 and 292 DF, p-value: 1.255e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL2_rank
Effect size...............: -0.084893
Standard error............: 0.053106
Odds ratio (effect size)..: 0.919
Lower 95% CI..............: 0.828
Upper 95% CI..............: 1.019
T-value...................: -1.598557
P-value...................: 0.1110005
R^2.......................: 0.193818
Adjusted r^2..............: 0.1386
Sample size of AE DB......: 2423
Sample size of model......: 313
Missing data %............: 87.08213
- processing IL4_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + SmokerStatus + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005
-0.407104 -0.108162 0.008971 0.194689 -0.237223 -0.818154 -0.359139
SmokerStatusEx-smoker SmokerStatusNever smoked Med.Statin.LLDyes
-0.247546 -0.004107 -0.170991
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.61406 -0.58571 0.00549 0.49758 2.56637
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.306618 0.800004 -0.383 0.7018
currentDF[, TRAIT] -0.105969 0.054651 -1.939 0.0535 .
Age 0.007144 0.006583 1.085 0.2788
Gendermale 0.194634 0.115078 1.691 0.0919 .
ORdate_year2003 -0.227735 0.159514 -1.428 0.1545
ORdate_year2004 -0.790034 0.153021 -5.163 4.73e-07 ***
ORdate_year2005 -0.324686 0.202934 -1.600 0.1108
Hypertension.compositeyes -0.062175 0.158502 -0.392 0.6952
DiabetesStatusDiabetes 0.039376 0.129650 0.304 0.7616
SmokerStatusEx-smoker -0.252396 0.114133 -2.211 0.0278 *
SmokerStatusNever smoked 0.040855 0.186835 0.219 0.8271
Med.Statin.LLDyes -0.187167 0.116881 -1.601 0.1105
Med.all.antiplateletyes -0.264907 0.194513 -1.362 0.1744
GFR_MDRD -0.001331 0.003142 -0.424 0.6722
BMI 0.003042 0.015479 0.197 0.8444
MedHx_CVDyes 0.107049 0.108386 0.988 0.3242
stenose70-90% 0.344021 0.293139 1.174 0.2416
stenose90-99% 0.221576 0.288654 0.768 0.4434
stenose100% (Occlusion) -0.587405 0.598186 -0.982 0.3270
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8438 on 269 degrees of freedom
Multiple R-squared: 0.1948, Adjusted R-squared: 0.1409
F-statistic: 3.614 on 18 and 269 DF, p-value: 2.078e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL4_rank
Effect size...............: -0.105969
Standard error............: 0.054651
Odds ratio (effect size)..: 0.899
Lower 95% CI..............: 0.808
Upper 95% CI..............: 1.001
T-value...................: -1.939002
P-value...................: 0.05354686
R^2.......................: 0.194753
Adjusted r^2..............: 0.14087
Sample size of AE DB......: 2423
Sample size of model......: 288
Missing data %............: 88.11391
- processing IL5_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + SmokerStatus + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005
-0.453248 -0.090663 0.009867 0.230869 -0.294584 -0.808770 -0.437064
SmokerStatusEx-smoker SmokerStatusNever smoked Med.Statin.LLDyes
-0.241517 -0.011822 -0.187352
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.69485 -0.54752 -0.01582 0.50239 2.51386
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.3539392 0.7606624 -0.465 0.6421
currentDF[, TRAIT] -0.0939063 0.0526489 -1.784 0.0755 .
Age 0.0081804 0.0064091 1.276 0.2029
Gendermale 0.2285324 0.1097279 2.083 0.0382 *
ORdate_year2003 -0.2920759 0.1582590 -1.846 0.0660 .
ORdate_year2004 -0.7888764 0.1508859 -5.228 3.29e-07 ***
ORdate_year2005 -0.4289436 0.1889564 -2.270 0.0239 *
Hypertension.compositeyes -0.0918686 0.1505360 -0.610 0.5422
DiabetesStatusDiabetes -0.0156638 0.1275370 -0.123 0.9023
SmokerStatusEx-smoker -0.2425791 0.1115488 -2.175 0.0305 *
SmokerStatusNever smoked 0.0354470 0.1840304 0.193 0.8474
Med.Statin.LLDyes -0.2002950 0.1118137 -1.791 0.0743 .
Med.all.antiplateletyes -0.2685498 0.1901351 -1.412 0.1589
GFR_MDRD -0.0009882 0.0029468 -0.335 0.7376
BMI 0.0052670 0.0146574 0.359 0.7196
MedHx_CVDyes 0.1382560 0.1041484 1.327 0.1854
stenose70-90% 0.2779623 0.2899467 0.959 0.3385
stenose90-99% 0.1493320 0.2880411 0.518 0.6045
stenose100% (Occlusion) -0.7447213 0.5975983 -1.246 0.2137
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8447 on 288 degrees of freedom
Multiple R-squared: 0.1782, Adjusted R-squared: 0.1269
F-statistic: 3.47 on 18 and 288 DF, p-value: 4.178e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL5_rank
Effect size...............: -0.093906
Standard error............: 0.052649
Odds ratio (effect size)..: 0.91
Lower 95% CI..............: 0.821
Upper 95% CI..............: 1.009
T-value...................: -1.783633
P-value...................: 0.07553624
R^2.......................: 0.178236
Adjusted r^2..............: 0.126876
Sample size of AE DB......: 2423
Sample size of model......: 307
Missing data %............: 87.32976
- processing IL6_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Gender + ORdate_year +
SmokerStatus, data = currentDF)
Coefficients:
(Intercept) Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.92224 0.01456 0.26615 -0.22980 -0.77480 -0.62387 1.38942
SmokerStatusEx-smoker SmokerStatusNever smoked
-0.28471 -0.10919
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.56359 -0.57954 -0.03632 0.51477 2.68325
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.560689 1.016945 -0.551 0.581813
currentDF[, TRAIT] 0.063622 0.053306 1.194 0.233621
Age 0.011803 0.006732 1.753 0.080572 .
Gendermale 0.270725 0.112671 2.403 0.016888 *
ORdate_year2003 -0.190529 0.161722 -1.178 0.239696
ORdate_year2004 -0.701769 0.152478 -4.602 6.21e-06 ***
ORdate_year2005 -0.636708 0.184224 -3.456 0.000628 ***
ORdate_year2006 1.509609 0.910620 1.658 0.098424 .
Hypertension.compositeyes -0.052531 0.157164 -0.334 0.738436
DiabetesStatusDiabetes 0.068873 0.130506 0.528 0.598079
SmokerStatusEx-smoker -0.262503 0.113946 -2.304 0.021932 *
SmokerStatusNever smoked -0.053266 0.184777 -0.288 0.773343
Med.Statin.LLDyes -0.175298 0.114250 -1.534 0.126017
Med.all.antiplateletyes -0.235903 0.180885 -1.304 0.193197
GFR_MDRD -0.001029 0.003020 -0.341 0.733549
BMI -0.005945 0.014333 -0.415 0.678578
MedHx_CVDyes 0.108408 0.106385 1.019 0.309030
stenose50-70% -0.038422 0.694021 -0.055 0.955888
stenose70-90% 0.417509 0.644932 0.647 0.517897
stenose90-99% 0.258147 0.641792 0.402 0.687807
stenose100% (Occlusion) -0.574627 0.858302 -0.669 0.503705
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8758 on 295 degrees of freedom
Multiple R-squared: 0.1917, Adjusted R-squared: 0.1369
F-statistic: 3.499 on 20 and 295 DF, p-value: 1.321e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL6_rank
Effect size...............: 0.063622
Standard error............: 0.053306
Odds ratio (effect size)..: 1.066
Lower 95% CI..............: 0.96
Upper 95% CI..............: 1.183
T-value...................: 1.193529
P-value...................: 0.2336211
R^2.......................: 0.191724
Adjusted r^2..............: 0.136926
Sample size of AE DB......: 2423
Sample size of model......: 316
Missing data %............: 86.95832
- processing IL8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes GFR_MDRD
0.554372 0.294365 0.252368 -0.328729 -0.882367 -0.920806 -0.185830 -0.178544 -0.005346
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.91736 -0.49060 -0.08618 0.46089 2.81577
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.554981 0.964756 0.575 0.56557
currentDF[, TRAIT] 0.281353 0.050945 5.523 7.50e-08 ***
Age 0.001574 0.006380 0.247 0.80527
Gendermale 0.293434 0.109783 2.673 0.00795 **
ORdate_year2003 -0.322502 0.156601 -2.059 0.04036 *
ORdate_year2004 -0.860867 0.147150 -5.850 1.34e-08 ***
ORdate_year2005 -0.927385 0.172866 -5.365 1.68e-07 ***
ORdate_year2006 -0.199368 0.505074 -0.395 0.69334
Hypertension.compositeyes -0.045573 0.153144 -0.298 0.76624
DiabetesStatusDiabetes 0.076864 0.125891 0.611 0.54198
SmokerStatusEx-smoker -0.117886 0.108474 -1.087 0.27806
SmokerStatusNever smoked 0.114740 0.181672 0.632 0.52817
Med.Statin.LLDyes -0.190216 0.108270 -1.757 0.08001 .
Med.all.antiplateletyes -0.019647 0.174092 -0.113 0.91023
GFR_MDRD -0.004492 0.002781 -1.615 0.10731
BMI -0.011503 0.013594 -0.846 0.39813
MedHx_CVDyes 0.076321 0.102228 0.747 0.45593
stenose50-70% -0.193942 0.660189 -0.294 0.76915
stenose70-90% 0.254973 0.609619 0.418 0.67608
stenose90-99% 0.108788 0.608713 0.179 0.85829
stenose100% (Occlusion) -0.217583 0.787102 -0.276 0.78241
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8316 on 286 degrees of freedom
Multiple R-squared: 0.2597, Adjusted R-squared: 0.2079
F-statistic: 5.017 on 20 and 286 DF, p-value: 1.288e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL8_rank
Effect size...............: 0.281353
Standard error............: 0.050945
Odds ratio (effect size)..: 1.325
Lower 95% CI..............: 1.199
Upper 95% CI..............: 1.464
T-value...................: 5.522689
P-value...................: 7.501523e-08
R^2.......................: 0.259707
Adjusted r^2..............: 0.207938
Sample size of AE DB......: 2423
Sample size of model......: 307
Missing data %............: 87.32976
- processing IL9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + DiabetesStatus + Med.Statin.LLD + BMI, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 DiabetesStatusDiabetes
0.55653 0.11659 0.30957 -0.22477 -0.85313 -0.73534 -0.22868 0.26738
Med.Statin.LLDyes BMI
-0.24925 -0.01917
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.59423 -0.62449 -0.06451 0.54038 2.83082
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.9669355 0.9678687 0.999 0.31852
currentDF[, TRAIT] 0.1160896 0.0505136 2.298 0.02219 *
Age -0.0005451 0.0063878 -0.085 0.93204
Gendermale 0.3278803 0.1098314 2.985 0.00305 **
ORdate_year2003 -0.2263603 0.1749118 -1.294 0.19654
ORdate_year2004 -0.8347318 0.1590787 -5.247 2.79e-07 ***
ORdate_year2005 -0.7219215 0.1628902 -4.432 1.28e-05 ***
ORdate_year2006 -0.2450410 0.2763557 -0.887 0.37590
Hypertension.compositeyes -0.0996332 0.1520396 -0.655 0.51273
DiabetesStatusDiabetes 0.2373546 0.1296141 1.831 0.06798 .
SmokerStatusEx-smoker -0.0886806 0.1121058 -0.791 0.42950
SmokerStatusNever smoked 0.0608124 0.1670330 0.364 0.71604
Med.Statin.LLDyes -0.2597901 0.1157483 -2.244 0.02548 *
Med.all.antiplateletyes -0.2055278 0.1838802 -1.118 0.26451
GFR_MDRD -0.0029336 0.0027772 -1.056 0.29161
BMI -0.0195662 0.0135133 -1.448 0.14860
MedHx_CVDyes 0.1273393 0.1045912 1.217 0.22430
stenose50-70% -0.1663630 0.7035082 -0.236 0.81321
stenose70-90% 0.1321344 0.6557527 0.202 0.84043
stenose90-99% 0.0475902 0.6530498 0.073 0.94195
stenose100% (Occlusion) -0.6902475 0.8108450 -0.851 0.39525
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9018 on 325 degrees of freedom
Multiple R-squared: 0.1878, Adjusted R-squared: 0.1378
F-statistic: 3.758 on 20 and 325 DF, p-value: 2.251e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL9_rank
Effect size...............: 0.11609
Standard error............: 0.050514
Odds ratio (effect size)..: 1.123
Lower 95% CI..............: 1.017
Upper 95% CI..............: 1.24
T-value...................: 2.298185
P-value...................: 0.0221858
R^2.......................: 0.187808
Adjusted r^2..............: 0.137827
Sample size of AE DB......: 2423
Sample size of model......: 346
Missing data %............: 85.72018
- processing IL10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Gender + ORdate_year +
SmokerStatus + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 SmokerStatusEx-smoker
-0.77944 0.01189 0.30810 -0.13734 -0.71388 -0.15778 -0.26320
SmokerStatusNever smoked Med.Statin.LLDyes
0.01930 -0.16099
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.58399 -0.54231 -0.01786 0.48273 2.42746
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.226620 0.829722 -0.273 0.78498
currentDF[, TRAIT] -0.059964 0.058163 -1.031 0.30353
Age 0.007879 0.006880 1.145 0.25319
Gendermale 0.311258 0.119050 2.615 0.00947 **
ORdate_year2003 -0.191545 0.163976 -1.168 0.24384
ORdate_year2004 -0.741826 0.157129 -4.721 3.87e-06 ***
ORdate_year2005 -0.186144 0.233271 -0.798 0.42563
Hypertension.compositeyes -0.135154 0.166412 -0.812 0.41745
DiabetesStatusDiabetes 0.009869 0.137043 0.072 0.94265
SmokerStatusEx-smoker -0.250359 0.118275 -2.117 0.03525 *
SmokerStatusNever smoked 0.074527 0.193184 0.386 0.69998
Med.Statin.LLDyes -0.179998 0.120321 -1.496 0.13589
Med.all.antiplateletyes -0.265044 0.203349 -1.303 0.19361
GFR_MDRD -0.002994 0.003254 -0.920 0.35833
BMI 0.004810 0.015668 0.307 0.75911
MedHx_CVDyes 0.046986 0.115232 0.408 0.68380
stenose70-90% 0.220286 0.315557 0.698 0.48576
stenose90-99% 0.136760 0.311260 0.439 0.66076
stenose100% (Occlusion) -0.716299 0.620450 -1.154 0.24938
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8583 on 256 degrees of freedom
Multiple R-squared: 0.1919, Adjusted R-squared: 0.1351
F-statistic: 3.377 on 18 and 256 DF, p-value: 8.284e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL10_rank
Effect size...............: -0.059964
Standard error............: 0.058163
Odds ratio (effect size)..: 0.942
Lower 95% CI..............: 0.84
Upper 95% CI..............: 1.056
T-value...................: -1.030963
P-value...................: 0.3035311
R^2.......................: 0.191875
Adjusted r^2..............: 0.135053
Sample size of AE DB......: 2423
Sample size of model......: 275
Missing data %............: 88.65043
- processing IL12_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Gender + ORdate_year +
SmokerStatus, data = currentDF)
Coefficients:
(Intercept) Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 SmokerStatusEx-smoker
-0.973526 0.013559 0.250300 -0.127146 -0.694916 -0.420144 -0.253300
SmokerStatusNever smoked
0.004165
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.57637 -0.55811 -0.02952 0.50298 2.47817
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.505251 0.830884 -0.608 0.5436
currentDF[, TRAIT] -0.051832 0.056750 -0.913 0.3619
Age 0.009362 0.006865 1.364 0.1738
Gendermale 0.255402 0.115818 2.205 0.0283 *
ORdate_year2003 -0.165654 0.166140 -0.997 0.3196
ORdate_year2004 -0.682148 0.155435 -4.389 1.64e-05 ***
ORdate_year2005 -0.430625 0.214194 -2.010 0.0454 *
Hypertension.compositeyes -0.151487 0.163434 -0.927 0.3548
DiabetesStatusDiabetes 0.058892 0.133475 0.441 0.6594
SmokerStatusEx-smoker -0.244131 0.118656 -2.057 0.0406 *
SmokerStatusNever smoked 0.060622 0.185613 0.327 0.7442
Med.Statin.LLDyes -0.155414 0.118500 -1.312 0.1908
Med.all.antiplateletyes -0.221457 0.198288 -1.117 0.2651
GFR_MDRD -0.002239 0.003164 -0.708 0.4798
BMI 0.002891 0.015735 0.184 0.8543
MedHx_CVDyes 0.076403 0.111258 0.687 0.4929
stenose70-90% 0.367375 0.296910 1.237 0.2170
stenose90-99% 0.240673 0.292633 0.822 0.4116
stenose100% (Occlusion) -0.586911 0.708415 -0.828 0.4081
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8565 on 268 degrees of freedom
Multiple R-squared: 0.1688, Adjusted R-squared: 0.1129
F-statistic: 3.023 on 18 and 268 DF, p-value: 5.348e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL12_rank
Effect size...............: -0.051832
Standard error............: 0.05675
Odds ratio (effect size)..: 0.949
Lower 95% CI..............: 0.85
Upper 95% CI..............: 1.061
T-value...................: -0.913343
P-value...................: 0.3618829
R^2.......................: 0.168767
Adjusted r^2..............: 0.112938
Sample size of AE DB......: 2423
Sample size of model......: 287
Missing data %............: 88.15518
- processing IL13_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.595506 0.153309 0.233674 -0.351780 -0.880565 -0.788063 -0.262755
Med.Statin.LLDyes Med.all.antiplateletyes GFR_MDRD
-0.176880 -0.237760 -0.003363
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.61842 -0.59184 -0.07764 0.51760 2.81177
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.7001545 0.9203854 0.761 0.44731
currentDF[, TRAIT] 0.1531729 0.0479158 3.197 0.00151 **
Age 0.0032401 0.0058251 0.556 0.57838
Gendermale 0.2476279 0.1005577 2.463 0.01425 *
ORdate_year2003 -0.3666446 0.1625877 -2.255 0.02471 *
ORdate_year2004 -0.8748938 0.1550510 -5.643 3.32e-08 ***
ORdate_year2005 -0.8155080 0.1611136 -5.062 6.55e-07 ***
ORdate_year2006 -0.2779367 0.2697125 -1.030 0.30345
Hypertension.compositeyes 0.0114096 0.1373345 0.083 0.93383
DiabetesStatusDiabetes 0.1240769 0.1148948 1.080 0.28088
SmokerStatusEx-smoker -0.1234959 0.1022583 -1.208 0.22794
SmokerStatusNever smoked 0.0005035 0.1574799 0.003 0.99745
Med.Statin.LLDyes -0.2036234 0.1060152 -1.921 0.05553 .
Med.all.antiplateletyes -0.2386655 0.1607695 -1.485 0.13852
GFR_MDRD -0.0022868 0.0026018 -0.879 0.38000
BMI -0.0153406 0.0124319 -1.234 0.21799
MedHx_CVDyes 0.0804634 0.0953409 0.844 0.39924
stenose50-70% -0.2776686 0.6849107 -0.405 0.68541
stenose70-90% 0.1099852 0.6433848 0.171 0.86436
stenose90-99% -0.0382385 0.6417982 -0.060 0.95252
stenose100% (Occlusion) -0.7175548 0.7920064 -0.906 0.36552
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8854 on 372 degrees of freedom
Multiple R-squared: 0.1747, Adjusted R-squared: 0.1303
F-statistic: 3.937 on 20 and 372 DF, p-value: 5.599e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL13_rank
Effect size...............: 0.153173
Standard error............: 0.047916
Odds ratio (effect size)..: 1.166
Lower 95% CI..............: 1.061
Upper 95% CI..............: 1.28
T-value...................: 3.196709
P-value...................: 0.001508995
R^2.......................: 0.174694
Adjusted r^2..............: 0.130323
Sample size of AE DB......: 2423
Sample size of model......: 393
Missing data %............: 83.78044
- processing IL21_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + Med.all.antiplatelet, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.3800 0.1516 0.2159 -0.3528 -0.8762 -0.7960 -0.2290
Med.Statin.LLDyes Med.all.antiplateletyes
-0.1771 -0.2458
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.60194 -0.59756 -0.08156 0.51003 2.82454
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.749161 0.920188 0.814 0.41608
currentDF[, TRAIT] 0.150103 0.048516 3.094 0.00212 **
Age 0.003030 0.005792 0.523 0.60126
Gendermale 0.238071 0.100429 2.371 0.01827 *
ORdate_year2003 -0.355965 0.161869 -2.199 0.02848 *
ORdate_year2004 -0.866419 0.154674 -5.602 4.13e-08 ***
ORdate_year2005 -0.799460 0.160420 -4.984 9.57e-07 ***
ORdate_year2006 -0.255847 0.269771 -0.948 0.34355
Hypertension.compositeyes 0.003424 0.137078 0.025 0.98009
DiabetesStatusDiabetes 0.112412 0.114645 0.981 0.32747
SmokerStatusEx-smoker -0.116677 0.101952 -1.144 0.25318
SmokerStatusNever smoked 0.001831 0.157382 0.012 0.99072
Med.Statin.LLDyes -0.207204 0.105736 -1.960 0.05078 .
Med.all.antiplateletyes -0.234722 0.160653 -1.461 0.14484
GFR_MDRD -0.002321 0.002600 -0.892 0.37274
BMI -0.015988 0.012420 -1.287 0.19879
MedHx_CVDyes 0.081885 0.095043 0.862 0.38948
stenose50-70% -0.299862 0.685199 -0.438 0.66191
stenose70-90% 0.097012 0.643566 0.151 0.88026
stenose90-99% -0.050900 0.642066 -0.079 0.93686
stenose100% (Occlusion) -0.764301 0.792037 -0.965 0.33518
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8851 on 373 degrees of freedom
Multiple R-squared: 0.1733, Adjusted R-squared: 0.129
F-statistic: 3.909 on 20 and 373 DF, p-value: 6.666e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL21_rank
Effect size...............: 0.150103
Standard error............: 0.048516
Odds ratio (effect size)..: 1.162
Lower 95% CI..............: 1.057
Upper 95% CI..............: 1.278
T-value...................: 3.093897
P-value...................: 0.002124423
R^2.......................: 0.173286
Adjusted r^2..............: 0.128959
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing INFG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Gender + ORdate_year +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet, data = currentDF)
Coefficients:
(Intercept) Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.347236 0.009089 0.332217 -0.199088 -0.771727 -0.636065 -0.976856
SmokerStatusEx-smoker SmokerStatusNever smoked Med.Statin.LLDyes Med.all.antiplateletyes
-0.195376 0.092847 -0.175474 -0.242653
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.56103 -0.57605 -0.00973 0.51068 2.53226
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.054204 1.005514 0.054 0.957048
currentDF[, TRAIT] -0.061677 0.060697 -1.016 0.310423
Age 0.005735 0.006706 0.855 0.393132
Gendermale 0.334824 0.115752 2.893 0.004115 **
ORdate_year2003 -0.235703 0.162992 -1.446 0.149248
ORdate_year2004 -0.779021 0.154956 -5.027 8.8e-07 ***
ORdate_year2005 -0.689029 0.199804 -3.449 0.000649 ***
ORdate_year2006 -1.118202 0.917737 -1.218 0.224067
Hypertension.compositeyes -0.098715 0.163613 -0.603 0.546760
DiabetesStatusDiabetes 0.058112 0.130999 0.444 0.657666
SmokerStatusEx-smoker -0.187955 0.115510 -1.627 0.104805
SmokerStatusNever smoked 0.157219 0.186945 0.841 0.401059
Med.Statin.LLDyes -0.222019 0.119391 -1.860 0.063973 .
Med.all.antiplateletyes -0.252868 0.182849 -1.383 0.167768
GFR_MDRD -0.002446 0.003002 -0.815 0.415883
BMI -0.008274 0.014225 -0.582 0.561273
MedHx_CVDyes 0.049977 0.111170 0.450 0.653377
stenose50-70% 0.067761 0.712924 0.095 0.924345
stenose70-90% 0.400194 0.651960 0.614 0.539816
stenose90-99% 0.280228 0.649499 0.431 0.666465
stenose100% (Occlusion) -0.617904 0.937357 -0.659 0.510301
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8801 on 285 degrees of freedom
Multiple R-squared: 0.1838, Adjusted R-squared: 0.1265
F-statistic: 3.209 on 20 and 285 DF, p-value: 8.033e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: INFG_rank
Effect size...............: -0.061677
Standard error............: 0.060697
Odds ratio (effect size)..: 0.94
Lower 95% CI..............: 0.835
Upper 95% CI..............: 1.059
T-value...................: -1.016143
P-value...................: 0.3104232
R^2.......................: 0.183783
Adjusted r^2..............: 0.126505
Sample size of AE DB......: 2423
Sample size of model......: 306
Missing data %............: 87.37103
- processing TNFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005
-0.1427 0.2506 -0.1883 -0.7477 -0.5064
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.67023 -0.54458 -0.02687 0.53600 2.47258
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.573232 0.816898 -0.702 0.4835
currentDF[, TRAIT] -0.074441 0.057518 -1.294 0.1967
Age 0.006849 0.006796 1.008 0.3145
Gendermale 0.281879 0.116890 2.411 0.0166 *
ORdate_year2003 -0.222083 0.169044 -1.314 0.1901
ORdate_year2004 -0.759360 0.164591 -4.614 6.22e-06 ***
ORdate_year2005 -0.493713 0.210463 -2.346 0.0197 *
Hypertension.compositeyes -0.082736 0.162229 -0.510 0.6105
DiabetesStatusDiabetes -0.054798 0.133730 -0.410 0.6823
SmokerStatusEx-smoker -0.191956 0.118628 -1.618 0.1068
SmokerStatusNever smoked 0.032888 0.189846 0.173 0.8626
Med.Statin.LLDyes -0.135460 0.118249 -1.146 0.2530
Med.all.antiplateletyes -0.121155 0.202175 -0.599 0.5495
GFR_MDRD -0.001717 0.003150 -0.545 0.5861
BMI 0.006974 0.015884 0.439 0.6610
MedHx_CVDyes 0.079350 0.112892 0.703 0.4828
stenose70-90% 0.306509 0.297962 1.029 0.3046
stenose90-99% 0.202746 0.293475 0.691 0.4903
stenose100% (Occlusion) -0.507828 0.709767 -0.715 0.4750
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8526 on 260 degrees of freedom
Multiple R-squared: 0.1656, Adjusted R-squared: 0.1079
F-statistic: 2.867 on 18 and 260 DF, p-value: 0.000127
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: TNFA_rank
Effect size...............: -0.074441
Standard error............: 0.057518
Odds ratio (effect size)..: 0.928
Lower 95% CI..............: 0.829
Upper 95% CI..............: 1.039
T-value...................: -1.294229
P-value...................: 0.1967348
R^2.......................: 0.165622
Adjusted r^2..............: 0.107857
Sample size of AE DB......: 2423
Sample size of model......: 279
Missing data %............: 88.48535
- processing MIF_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year + Med.Statin.LLD +
GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes GFR_MDRD
0.319612 0.266668 -0.271782 -0.784781 -0.704787 -0.257963 -0.185071 -0.003707
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.55472 -0.59428 -0.06925 0.51634 3.00948
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.650392 0.930469 0.699 0.48499
currentDF[, TRAIT] 0.052491 0.054480 0.963 0.33593
Age 0.001129 0.005829 0.194 0.84650
Gendermale 0.264589 0.101381 2.610 0.00942 **
ORdate_year2003 -0.293023 0.162374 -1.805 0.07194 .
ORdate_year2004 -0.738720 0.161606 -4.571 6.61e-06 ***
ORdate_year2005 -0.690712 0.166985 -4.136 4.36e-05 ***
ORdate_year2006 -0.228685 0.279617 -0.818 0.41397
Hypertension.compositeyes -0.006436 0.138672 -0.046 0.96301
DiabetesStatusDiabetes 0.114697 0.117042 0.980 0.32774
SmokerStatusEx-smoker -0.083487 0.103258 -0.809 0.41930
SmokerStatusNever smoked 0.068765 0.158166 0.435 0.66398
Med.Statin.LLDyes -0.207867 0.106976 -1.943 0.05276 .
Med.all.antiplateletyes -0.227337 0.162915 -1.395 0.16371
GFR_MDRD -0.002390 0.002655 -0.900 0.36860
BMI -0.017478 0.012585 -1.389 0.16573
MedHx_CVDyes 0.082504 0.096351 0.856 0.39239
stenose50-70% -0.176613 0.692245 -0.255 0.79876
stenose70-90% 0.238119 0.649635 0.367 0.71417
stenose90-99% 0.098505 0.648168 0.152 0.87929
stenose100% (Occlusion) -0.707803 0.801294 -0.883 0.37763
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8952 on 373 degrees of freedom
Multiple R-squared: 0.1542, Adjusted R-squared: 0.1088
F-statistic: 3.399 on 20 and 373 DF, p-value: 1.723e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MIF_rank
Effect size...............: 0.052491
Standard error............: 0.05448
Odds ratio (effect size)..: 1.054
Lower 95% CI..............: 0.947
Upper 95% CI..............: 1.173
T-value...................: 0.963492
P-value...................: 0.3359251
R^2.......................: 0.154176
Adjusted r^2..............: 0.108823
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing MCP1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes GFR_MDRD
0.253585 0.226533 0.227600 -0.250397 -0.660326 -0.571008 -0.046719 -0.167501 -0.003966
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.85025 -0.58290 -0.06178 0.54670 2.92347
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.221141 0.905701 0.244 0.807239
currentDF[, TRAIT] 0.225405 0.045153 4.992 9.23e-07 ***
Age 0.002192 0.005699 0.385 0.700753
Gendermale 0.227011 0.099167 2.289 0.022634 *
ORdate_year2003 -0.259157 0.157871 -1.642 0.101530
ORdate_year2004 -0.660756 0.150901 -4.379 1.56e-05 ***
ORdate_year2005 -0.599515 0.157477 -3.807 0.000165 ***
ORdate_year2006 -0.074874 0.266850 -0.281 0.779186
Hypertension.compositeyes 0.023825 0.134533 0.177 0.859531
DiabetesStatusDiabetes 0.158528 0.113451 1.397 0.163157
SmokerStatusEx-smoker -0.089651 0.099943 -0.897 0.370292
SmokerStatusNever smoked 0.020878 0.153166 0.136 0.891652
Med.Statin.LLDyes -0.182543 0.103891 -1.757 0.079735 .
Med.all.antiplateletyes -0.191017 0.161585 -1.182 0.237911
GFR_MDRD -0.002696 0.002548 -1.058 0.290742
BMI -0.011309 0.012244 -0.924 0.356254
MedHx_CVDyes 0.066293 0.093651 0.708 0.479475
stenose50-70% -0.069976 0.667715 -0.105 0.916592
stenose70-90% 0.319651 0.626256 0.510 0.610065
stenose90-99% 0.215407 0.624364 0.345 0.730289
stenose100% (Occlusion) -0.342039 0.776118 -0.441 0.659685
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8647 on 369 degrees of freedom
Multiple R-squared: 0.2036, Adjusted R-squared: 0.1604
F-statistic: 4.716 on 20 and 369 DF, p-value: 3.694e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MCP1_rank
Effect size...............: 0.225405
Standard error............: 0.045153
Odds ratio (effect size)..: 1.253
Lower 95% CI..............: 1.147
Upper 95% CI..............: 1.369
T-value...................: 4.991991
P-value...................: 9.231458e-07
R^2.......................: 0.203569
Adjusted r^2..............: 0.160402
Sample size of AE DB......: 2423
Sample size of model......: 390
Missing data %............: 83.90425
- processing MIP1a_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + DiabetesStatus + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 DiabetesStatusDiabetes
0.08618 0.16039 0.27647 -0.26419 -0.86729 -0.74525 -0.18002 0.17373
Med.Statin.LLDyes
-0.24536
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.58560 -0.62123 -0.07518 0.51911 2.81207
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.9629414 0.9471188 1.017 0.31002
currentDF[, TRAIT] 0.1644586 0.0506263 3.248 0.00128 **
Age 0.0002706 0.0062095 0.044 0.96527
Gendermale 0.2900835 0.1078236 2.690 0.00749 **
ORdate_year2003 -0.2788134 0.1689739 -1.650 0.09987 .
ORdate_year2004 -0.8678387 0.1573600 -5.515 6.97e-08 ***
ORdate_year2005 -0.7504245 0.1611493 -4.657 4.63e-06 ***
ORdate_year2006 -0.2041206 0.2733388 -0.747 0.45573
Hypertension.compositeyes -0.0905211 0.1464746 -0.618 0.53699
DiabetesStatusDiabetes 0.1884920 0.1229651 1.533 0.12624
SmokerStatusEx-smoker -0.1267543 0.1089653 -1.163 0.24555
SmokerStatusNever smoked 0.0270176 0.1634002 0.165 0.86877
Med.Statin.LLDyes -0.2561369 0.1127291 -2.272 0.02371 *
Med.all.antiplateletyes -0.1992514 0.1784260 -1.117 0.26491
GFR_MDRD -0.0022485 0.0026872 -0.837 0.40334
BMI -0.0161466 0.0129984 -1.242 0.21503
MedHx_CVDyes 0.1031834 0.1018672 1.013 0.31183
stenose50-70% -0.2733916 0.6976237 -0.392 0.69539
stenose70-90% 0.0186183 0.6506549 0.029 0.97719
stenose90-99% -0.0787949 0.6486271 -0.121 0.90338
stenose100% (Occlusion) -0.7800117 0.8025164 -0.972 0.33177
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8923 on 336 degrees of freedom
Multiple R-squared: 0.1919, Adjusted R-squared: 0.1438
F-statistic: 3.99 on 20 and 336 DF, p-value: 4.874e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.164459
Standard error............: 0.050626
Odds ratio (effect size)..: 1.179
Lower 95% CI..............: 1.067
Upper 95% CI..............: 1.302
T-value...................: 3.248481
P-value...................: 0.001277402
R^2.......................: 0.191936
Adjusted r^2..............: 0.143837
Sample size of AE DB......: 2423
Sample size of model......: 357
Missing data %............: 85.2662
- processing RANTES_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year + Med.Statin.LLD +
GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes GFR_MDRD
0.345942 0.266352 -0.293457 -0.771033 -0.700843 -0.250994 -0.205769 -0.003899
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.5662 -0.5883 -0.0691 0.5373 2.9849
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.671235 0.933661 0.719 0.472644
currentDF[, TRAIT] 0.059297 0.052100 1.138 0.255809
Age 0.001005 0.005884 0.171 0.864452
Gendermale 0.260366 0.102887 2.531 0.011806 *
ORdate_year2003 -0.275189 0.167530 -1.643 0.101318
ORdate_year2004 -0.695159 0.169903 -4.092 5.28e-05 ***
ORdate_year2005 -0.665364 0.171276 -3.885 0.000122 ***
ORdate_year2006 -0.218455 0.278730 -0.784 0.433694
Hypertension.compositeyes -0.069846 0.140580 -0.497 0.619598
DiabetesStatusDiabetes 0.142747 0.118522 1.204 0.229218
SmokerStatusEx-smoker -0.069088 0.103477 -0.668 0.504764
SmokerStatusNever smoked 0.078300 0.158158 0.495 0.620845
Med.Statin.LLDyes -0.220823 0.107543 -2.053 0.040749 *
Med.all.antiplateletyes -0.193492 0.167319 -1.156 0.248262
GFR_MDRD -0.002931 0.002638 -1.111 0.267336
BMI -0.017209 0.012643 -1.361 0.174302
MedHx_CVDyes 0.093698 0.097328 0.963 0.336334
stenose50-70% -0.165364 0.693095 -0.239 0.811559
stenose70-90% 0.221135 0.647151 0.342 0.732769
stenose90-99% 0.123034 0.644778 0.191 0.848775
stenose100% (Occlusion) -0.747078 0.801401 -0.932 0.351840
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8924 on 366 degrees of freedom
Multiple R-squared: 0.1519, Adjusted R-squared: 0.1056
F-statistic: 3.279 on 20 and 366 DF, p-value: 3.775e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: RANTES_rank
Effect size...............: 0.059297
Standard error............: 0.0521
Odds ratio (effect size)..: 1.061
Lower 95% CI..............: 0.958
Upper 95% CI..............: 1.175
T-value...................: 1.138134
P-value...................: 0.2558089
R^2.......................: 0.151935
Adjusted r^2..............: 0.105593
Sample size of AE DB......: 2423
Sample size of model......: 387
Missing data %............: 84.02806
- processing MIG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + Med.all.antiplatelet, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.39362 0.09763 0.23101 -0.26131 -0.86028 -0.79588 -0.30495
Med.Statin.LLDyes Med.all.antiplateletyes
-0.19293 -0.27025
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.60978 -0.59721 -0.08116 0.54150 2.88099
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.8350740 0.9369059 0.891 0.3734
currentDF[, TRAIT] 0.1022259 0.0514028 1.989 0.0475 *
Age 0.0020825 0.0060071 0.347 0.7290
Gendermale 0.2505409 0.1028871 2.435 0.0154 *
ORdate_year2003 -0.2812890 0.1634493 -1.721 0.0861 .
ORdate_year2004 -0.8636556 0.1594731 -5.416 1.12e-07 ***
ORdate_year2005 -0.8101315 0.1654979 -4.895 1.48e-06 ***
ORdate_year2006 -0.3462155 0.2738780 -1.264 0.2070
Hypertension.compositeyes -0.0347969 0.1407324 -0.247 0.8049
DiabetesStatusDiabetes 0.1438794 0.1183520 1.216 0.2249
SmokerStatusEx-smoker -0.1081636 0.1046604 -1.033 0.3021
SmokerStatusNever smoked 0.0005493 0.1624450 0.003 0.9973
Med.Statin.LLDyes -0.2229684 0.1082118 -2.060 0.0401 *
Med.all.antiplateletyes -0.2725158 0.1652038 -1.650 0.0999 .
GFR_MDRD -0.0024015 0.0026435 -0.908 0.3642
BMI -0.0157149 0.0126482 -1.242 0.2149
MedHx_CVDyes 0.0601735 0.0980827 0.613 0.5399
stenose50-70% -0.2448441 0.6956385 -0.352 0.7251
stenose70-90% 0.1291190 0.6504024 0.199 0.8427
stenose90-99% 0.0125920 0.6486416 0.019 0.9845
stenose100% (Occlusion) -0.8077337 0.8018032 -1.007 0.3144
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8945 on 362 degrees of freedom
Multiple R-squared: 0.1633, Adjusted R-squared: 0.1171
F-statistic: 3.534 on 20 and 362 DF, p-value: 7.702e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MIG_rank
Effect size...............: 0.102226
Standard error............: 0.051403
Odds ratio (effect size)..: 1.108
Lower 95% CI..............: 1.001
Upper 95% CI..............: 1.225
T-value...................: 1.988722
P-value...................: 0.04748441
R^2.......................: 0.163336
Adjusted r^2..............: 0.117111
Sample size of AE DB......: 2423
Sample size of model......: 383
Missing data %............: 84.19315
- processing IP10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + DiabetesStatus + Med.Statin.LLD + MedHx_CVD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 DiabetesStatusDiabetes
-0.05196 0.13730 0.28889 -0.31572 -0.84836 -0.72740 -0.25744 0.17767
Med.Statin.LLDyes MedHx_CVDyes
-0.22853 0.15677
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.53175 -0.55291 -0.04647 0.57922 2.85591
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.716328 0.959014 0.747 0.45563
currentDF[, TRAIT] 0.142418 0.049908 2.854 0.00460 **
Age 0.001330 0.006296 0.211 0.83281
Gendermale 0.296415 0.106780 2.776 0.00582 **
ORdate_year2003 -0.331883 0.168242 -1.973 0.04937 *
ORdate_year2004 -0.854167 0.156550 -5.456 9.58e-08 ***
ORdate_year2005 -0.717619 0.161502 -4.443 1.21e-05 ***
ORdate_year2006 -0.295784 0.287884 -1.027 0.30497
Hypertension.compositeyes -0.043990 0.146477 -0.300 0.76412
DiabetesStatusDiabetes 0.184435 0.124507 1.481 0.13948
SmokerStatusEx-smoker -0.110128 0.110085 -1.000 0.31786
SmokerStatusNever smoked -0.072067 0.171293 -0.421 0.67423
Med.Statin.LLDyes -0.238192 0.112473 -2.118 0.03494 *
Med.all.antiplateletyes -0.191710 0.169583 -1.130 0.25910
GFR_MDRD -0.003092 0.002747 -1.125 0.26126
BMI -0.016701 0.013170 -1.268 0.20565
MedHx_CVDyes 0.151075 0.101883 1.483 0.13908
stenose50-70% -0.168247 0.691547 -0.243 0.80793
stenose70-90% 0.167960 0.646886 0.260 0.79530
stenose90-99% 0.056514 0.644255 0.088 0.93015
stenose100% (Occlusion) -0.549828 0.798350 -0.689 0.49149
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8894 on 329 degrees of freedom
Multiple R-squared: 0.1818, Adjusted R-squared: 0.132
F-statistic: 3.654 on 20 and 329 DF, p-value: 4.204e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IP10_rank
Effect size...............: 0.142418
Standard error............: 0.049908
Odds ratio (effect size)..: 1.153
Lower 95% CI..............: 1.046
Upper 95% CI..............: 1.272
T-value...................: 2.853631
P-value...................: 0.004596121
R^2.......................: 0.181762
Adjusted r^2..............: 0.132021
Sample size of AE DB......: 2423
Sample size of model......: 350
Missing data %............: 85.5551
- processing Eotaxin1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + Med.all.antiplatelet, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.3647 0.1365 0.2092 -0.3042 -0.8738 -0.7910 -0.2495
Med.Statin.LLDyes Med.all.antiplateletyes
-0.1702 -0.2428
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.66607 -0.60520 -0.08781 0.52891 2.88049
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.760289 0.923674 0.823 0.41097
currentDF[, TRAIT] 0.131722 0.049992 2.635 0.00877 **
Age 0.002409 0.005800 0.415 0.67814
Gendermale 0.232758 0.101319 2.297 0.02216 *
ORdate_year2003 -0.309361 0.161219 -1.919 0.05576 .
ORdate_year2004 -0.862456 0.155739 -5.538 5.79e-08 ***
ORdate_year2005 -0.793011 0.161133 -4.921 1.29e-06 ***
ORdate_year2006 -0.276359 0.270536 -1.022 0.30767
Hypertension.compositeyes -0.001597 0.137517 -0.012 0.99074
DiabetesStatusDiabetes 0.106383 0.114993 0.925 0.35550
SmokerStatusEx-smoker -0.111915 0.102269 -1.094 0.27452
SmokerStatusNever smoked 0.007022 0.158094 0.044 0.96460
Med.Statin.LLDyes -0.201194 0.106114 -1.896 0.05873 .
Med.all.antiplateletyes -0.235044 0.161263 -1.458 0.14582
GFR_MDRD -0.002307 0.002611 -0.884 0.37752
BMI -0.016028 0.012463 -1.286 0.19924
MedHx_CVDyes 0.081617 0.095385 0.856 0.39273
stenose50-70% -0.277037 0.687591 -0.403 0.68725
stenose70-90% 0.116665 0.646032 0.181 0.85679
stenose90-99% -0.029303 0.644561 -0.045 0.96376
stenose100% (Occlusion) -0.775388 0.795151 -0.975 0.33012
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8881 on 373 degrees of freedom
Multiple R-squared: 0.1676, Adjusted R-squared: 0.1229
F-statistic: 3.754 on 20 and 373 DF, p-value: 1.804e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.131722
Standard error............: 0.049992
Odds ratio (effect size)..: 1.141
Lower 95% CI..............: 1.034
Upper 95% CI..............: 1.258
T-value...................: 2.634876
P-value...................: 0.008767672
R^2.......................: 0.167565
Adjusted r^2..............: 0.12293
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing TARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes GFR_MDRD
0.445317 0.100312 0.228664 -0.374751 -0.826385 -0.726545 -0.278725 -0.255280 -0.003695
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.5800 -0.5851 -0.1003 0.5449 2.7682
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.2456953 0.9833857 1.267 0.206184
currentDF[, TRAIT] 0.0942966 0.0530131 1.779 0.076247 .
Age -0.0003196 0.0062250 -0.051 0.959086
Gendermale 0.2367579 0.1088728 2.175 0.030402 *
ORdate_year2003 -0.4236892 0.2590966 -1.635 0.102994
ORdate_year2004 -0.8570383 0.2545802 -3.366 0.000856 ***
ORdate_year2005 -0.7784256 0.2594474 -3.000 0.002912 **
ORdate_year2006 -0.3552240 0.3381685 -1.050 0.294323
Hypertension.compositeyes -0.1431015 0.1469515 -0.974 0.330902
DiabetesStatusDiabetes 0.1181421 0.1258904 0.938 0.348731
SmokerStatusEx-smoker -0.0404679 0.1112516 -0.364 0.716288
SmokerStatusNever smoked 0.0579784 0.1647016 0.352 0.725058
Med.Statin.LLDyes -0.2810100 0.1178306 -2.385 0.017677 *
Med.all.antiplateletyes -0.1440247 0.1755973 -0.820 0.412723
GFR_MDRD -0.0037807 0.0028874 -1.309 0.191361
BMI -0.0214379 0.0137193 -1.563 0.119150
MedHx_CVDyes 0.0332867 0.1046612 0.318 0.750664
stenose50-70% -0.1989466 0.6936583 -0.287 0.774448
stenose70-90% 0.1549955 0.6458604 0.240 0.810500
stenose90-99% 0.0453677 0.6440882 0.070 0.943891
stenose100% (Occlusion) -0.7696348 0.8002061 -0.962 0.336890
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8889 on 315 degrees of freedom
Multiple R-squared: 0.1497, Adjusted R-squared: 0.09567
F-statistic: 2.772 on 20 and 315 DF, p-value: 9.627e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: TARC_rank
Effect size...............: 0.094297
Standard error............: 0.053013
Odds ratio (effect size)..: 1.099
Lower 95% CI..............: 0.99
Upper 95% CI..............: 1.219
T-value...................: 1.77874
P-value...................: 0.0762468
R^2.......................: 0.149661
Adjusted r^2..............: 0.095671
Sample size of AE DB......: 2423
Sample size of model......: 336
Missing data %............: 86.13289
- processing PARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes
0.01341 0.08601 0.25787 -0.26625 -0.69432 -0.65723 -0.16118 -0.18550
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.57766 -0.60627 -0.07318 0.52362 3.04525
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.532447 0.935102 0.569 0.56943
currentDF[, TRAIT] 0.066460 0.051642 1.287 0.19891
Age 0.001022 0.005809 0.176 0.86043
Gendermale 0.272089 0.100812 2.699 0.00727 **
ORdate_year2003 -0.275106 0.162707 -1.691 0.09171 .
ORdate_year2004 -0.714099 0.163687 -4.363 1.67e-05 ***
ORdate_year2005 -0.680108 0.166043 -4.096 5.16e-05 ***
ORdate_year2006 -0.220923 0.277398 -0.796 0.42630
Hypertension.compositeyes 0.003680 0.138944 0.026 0.97888
DiabetesStatusDiabetes 0.115791 0.116513 0.994 0.32096
SmokerStatusEx-smoker -0.091081 0.102730 -0.887 0.37586
SmokerStatusNever smoked 0.054335 0.157935 0.344 0.73102
Med.Statin.LLDyes -0.207709 0.106859 -1.944 0.05268 .
Med.all.antiplateletyes -0.198629 0.162670 -1.221 0.22284
GFR_MDRD -0.002466 0.002634 -0.936 0.34982
BMI -0.016689 0.012548 -1.330 0.18434
MedHx_CVDyes 0.084444 0.096082 0.879 0.38003
stenose50-70% -0.101489 0.690909 -0.147 0.88330
stenose70-90% 0.292377 0.647679 0.451 0.65195
stenose90-99% 0.155666 0.645624 0.241 0.80960
stenose100% (Occlusion) -0.588893 0.802714 -0.734 0.46364
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8944 on 373 degrees of freedom
Multiple R-squared: 0.1558, Adjusted R-squared: 0.1106
F-statistic: 3.442 on 20 and 373 DF, p-value: 1.313e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: PARC_rank
Effect size...............: 0.06646
Standard error............: 0.051642
Odds ratio (effect size)..: 1.069
Lower 95% CI..............: 0.966
Upper 95% CI..............: 1.183
T-value...................: 1.286948
P-value...................: 0.1989104
R^2.......................: 0.155819
Adjusted r^2..............: 0.110555
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing MDC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + DiabetesStatus + Med.Statin.LLD + BMI, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 DiabetesStatusDiabetes
0.50428 0.07524 0.30695 -0.21831 -0.77565 -0.67567 -0.17882 0.24380
Med.Statin.LLDyes BMI
-0.25477 -0.01886
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.5738 -0.6319 -0.0623 0.4945 2.8978
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.025396 0.956290 1.072 0.28438
currentDF[, TRAIT] 0.063063 0.053183 1.186 0.23655
Age -0.002046 0.006167 -0.332 0.74023
Gendermale 0.326899 0.108395 3.016 0.00276 **
ORdate_year2003 -0.230096 0.173634 -1.325 0.18602
ORdate_year2004 -0.770880 0.164282 -4.692 3.95e-06 ***
ORdate_year2005 -0.676680 0.170394 -3.971 8.76e-05 ***
ORdate_year2006 -0.213978 0.283434 -0.755 0.45081
Hypertension.compositeyes -0.095521 0.148555 -0.643 0.52067
DiabetesStatusDiabetes 0.216151 0.125912 1.717 0.08697 .
SmokerStatusEx-smoker -0.070028 0.109517 -0.639 0.52298
SmokerStatusNever smoked 0.083892 0.164933 0.509 0.61134
Med.Statin.LLDyes -0.265717 0.113240 -2.346 0.01954 *
Med.all.antiplateletyes -0.205270 0.183545 -1.118 0.26422
GFR_MDRD -0.002951 0.002744 -1.075 0.28295
BMI -0.020241 0.013169 -1.537 0.12524
MedHx_CVDyes 0.118820 0.102939 1.154 0.24921
stenose50-70% -0.145014 0.702054 -0.207 0.83648
stenose70-90% 0.151051 0.654487 0.231 0.81762
stenose90-99% 0.065741 0.652519 0.101 0.91981
stenose100% (Occlusion) -0.733400 0.808462 -0.907 0.36498
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8995 on 333 degrees of freedom
Multiple R-squared: 0.1783, Adjusted R-squared: 0.1289
F-statistic: 3.612 on 20 and 333 DF, p-value: 5.369e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MDC_rank
Effect size...............: 0.063063
Standard error............: 0.053183
Odds ratio (effect size)..: 1.065
Lower 95% CI..............: 0.96
Upper 95% CI..............: 1.182
T-value...................: 1.185783
P-value...................: 0.2365535
R^2.......................: 0.178256
Adjusted r^2..............: 0.128902
Sample size of AE DB......: 2423
Sample size of model......: 354
Missing data %............: 85.39001
- processing OPG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.537737 0.153728 0.224314 -0.240409 -0.726714 -0.696086 -0.264687
Med.Statin.LLDyes Med.all.antiplateletyes GFR_MDRD
-0.170890 -0.253921 -0.003719
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.43705 -0.58842 -0.07866 0.52581 2.79285
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.502337 0.920778 0.546 0.58570
currentDF[, TRAIT] 0.151590 0.045783 3.311 0.00102 **
Age 0.002381 0.005785 0.412 0.68087
Gendermale 0.241130 0.100618 2.396 0.01705 *
ORdate_year2003 -0.253826 0.161234 -1.574 0.11627
ORdate_year2004 -0.720852 0.153602 -4.693 3.79e-06 ***
ORdate_year2005 -0.721058 0.158984 -4.535 7.77e-06 ***
ORdate_year2006 -0.276013 0.269457 -1.024 0.30634
Hypertension.compositeyes 0.028447 0.137553 0.207 0.83627
DiabetesStatusDiabetes 0.131431 0.114933 1.144 0.25355
SmokerStatusEx-smoker -0.118901 0.102016 -1.166 0.24456
SmokerStatusNever smoked 0.026289 0.156520 0.168 0.86670
Med.Statin.LLDyes -0.198611 0.105927 -1.875 0.06158 .
Med.all.antiplateletyes -0.251184 0.160817 -1.562 0.11916
GFR_MDRD -0.002673 0.002595 -1.030 0.30375
BMI -0.014431 0.012431 -1.161 0.24644
MedHx_CVDyes 0.087307 0.095204 0.917 0.35971
stenose50-70% -0.147076 0.682827 -0.215 0.82958
stenose70-90% 0.250920 0.640614 0.392 0.69551
stenose90-99% 0.109161 0.638646 0.171 0.86437
stenose100% (Occlusion) -0.525487 0.792416 -0.663 0.50765
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8846 on 372 degrees of freedom
Multiple R-squared: 0.1763, Adjusted R-squared: 0.132
F-statistic: 3.981 on 20 and 372 DF, p-value: 4.221e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: OPG_rank
Effect size...............: 0.15159
Standard error............: 0.045783
Odds ratio (effect size)..: 1.164
Lower 95% CI..............: 1.064
Upper 95% CI..............: 1.273
T-value...................: 3.311041
P-value...................: 0.001020455
R^2.......................: 0.176297
Adjusted r^2..............: 0.132012
Sample size of AE DB......: 2423
Sample size of model......: 393
Missing data %............: 83.78044
- processing sICAM1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + Med.all.antiplatelet, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.2322 0.1319 0.2406 -0.2980 -0.7209 -0.6714 -0.1267
Med.Statin.LLDyes Med.all.antiplateletyes
-0.1670 -0.2229
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.58122 -0.59327 -0.07815 0.47692 2.94726
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.535689 0.923791 0.580 0.56235
currentDF[, TRAIT] 0.128976 0.047814 2.697 0.00730 **
Age 0.003054 0.005827 0.524 0.60051
Gendermale 0.261894 0.100154 2.615 0.00929 **
ORdate_year2003 -0.305607 0.161091 -1.897 0.05858 .
ORdate_year2004 -0.717583 0.154948 -4.631 5.03e-06 ***
ORdate_year2005 -0.683182 0.160602 -4.254 2.66e-05 ***
ORdate_year2006 -0.169657 0.273938 -0.619 0.53608
Hypertension.compositeyes -0.010850 0.137404 -0.079 0.93711
DiabetesStatusDiabetes 0.128256 0.115423 1.111 0.26721
SmokerStatusEx-smoker -0.092908 0.101962 -0.911 0.36277
SmokerStatusNever smoked 0.034210 0.156992 0.218 0.82762
Med.Statin.LLDyes -0.194881 0.106128 -1.836 0.06711 .
Med.all.antiplateletyes -0.210397 0.161010 -1.307 0.19211
GFR_MDRD -0.002104 0.002616 -0.804 0.42167
BMI -0.017581 0.012459 -1.411 0.15906
MedHx_CVDyes 0.065733 0.095694 0.687 0.49257
stenose50-70% -0.232299 0.686144 -0.339 0.73513
stenose70-90% 0.194280 0.643567 0.302 0.76291
stenose90-99% 0.051038 0.641785 0.080 0.93666
stenose100% (Occlusion) -0.663489 0.793925 -0.836 0.40385
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8877 on 373 degrees of freedom
Multiple R-squared: 0.1683, Adjusted R-squared: 0.1237
F-statistic: 3.774 on 20 and 373 DF, p-value: 1.59e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: sICAM1_rank
Effect size...............: 0.128976
Standard error............: 0.047814
Odds ratio (effect size)..: 1.138
Lower 95% CI..............: 1.036
Upper 95% CI..............: 1.249
T-value...................: 2.697445
P-value...................: 0.007304536
R^2.......................: 0.168295
Adjusted r^2..............: 0.1237
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing VEGFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.all.antiplatelet + GFR_MDRD + BMI + stenose,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.299744 0.234674 0.254394 -0.499441 -0.912211 -1.190647 -0.630062
Med.all.antiplateletyes GFR_MDRD BMI stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.263382 -0.004663 -0.022174 0.787113 1.158240 0.972748 0.149026
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.51431 -0.51240 -0.08887 0.42123 2.68181
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.4466456 1.0882270 0.410 0.68177
currentDF[, TRAIT] 0.2255962 0.0517761 4.357 1.80e-05 ***
Age 0.0012584 0.0059104 0.213 0.83153
Gendermale 0.2790519 0.1032011 2.704 0.00723 **
ORdate_year2003 -0.5119534 0.1791660 -2.857 0.00456 **
ORdate_year2004 -0.9075844 0.1667312 -5.443 1.07e-07 ***
ORdate_year2005 -1.1992578 0.1923508 -6.235 1.48e-09 ***
ORdate_year2006 -0.6011263 0.2918672 -2.060 0.04028 *
Hypertension.compositeyes -0.1158818 0.1446547 -0.801 0.42369
DiabetesStatusDiabetes -0.0005099 0.1158642 -0.004 0.99649
SmokerStatusEx-smoker -0.1115478 0.1034967 -1.078 0.28197
SmokerStatusNever smoked 0.1514999 0.1645471 0.921 0.35792
Med.Statin.LLDyes -0.1040645 0.1070538 -0.972 0.33177
Med.all.antiplateletyes -0.2447287 0.1557966 -1.571 0.11725
GFR_MDRD -0.0048051 0.0025205 -1.906 0.05752 .
BMI -0.0215404 0.0129115 -1.668 0.09627 .
MedHx_CVDyes 0.0534435 0.0984811 0.543 0.58774
stenose50-70% 0.6816893 0.8742307 0.780 0.43613
stenose70-90% 1.0790306 0.8381453 1.287 0.19892
stenose90-99% 0.8728519 0.8369689 1.043 0.29782
stenose100% (Occlusion) 0.0174804 0.9434566 0.019 0.98523
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8261 on 309 degrees of freedom
Multiple R-squared: 0.2443, Adjusted R-squared: 0.1954
F-statistic: 4.994 on 20 and 309 DF, p-value: 1.109e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: VEGFA_rank
Effect size...............: 0.225596
Standard error............: 0.051776
Odds ratio (effect size)..: 1.253
Lower 95% CI..............: 1.132
Upper 95% CI..............: 1.387
T-value...................: 4.357153
P-value...................: 1.795496e-05
R^2.......................: 0.244292
Adjusted r^2..............: 0.195379
Sample size of AE DB......: 2423
Sample size of model......: 330
Missing data %............: 86.38052
- processing TGFB_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year + Med.Statin.LLD +
GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes GFR_MDRD
0.326961 0.236711 -0.221634 -0.767666 -0.691532 -0.175441 -0.167351 -0.003893
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.57154 -0.61829 -0.05864 0.53451 3.00250
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.6686460 0.9459532 0.707 0.4801
currentDF[, TRAIT] 0.0304782 0.0486423 0.627 0.5313
Age 0.0001302 0.0059898 0.022 0.9827
Gendermale 0.2515167 0.1043338 2.411 0.0164 *
ORdate_year2003 -0.2281038 0.1640758 -1.390 0.1653
ORdate_year2004 -0.7388702 0.1599674 -4.619 5.37e-06 ***
ORdate_year2005 -0.6968379 0.1638422 -4.253 2.69e-05 ***
ORdate_year2006 -0.2103473 0.3024671 -0.695 0.4872
Hypertension.compositeyes -0.0254239 0.1411116 -0.180 0.8571
DiabetesStatusDiabetes 0.0922198 0.1178893 0.782 0.4346
SmokerStatusEx-smoker -0.0743725 0.1048733 -0.709 0.4787
SmokerStatusNever smoked 0.1237773 0.1629165 0.760 0.4479
Med.Statin.LLDyes -0.1825671 0.1094023 -1.669 0.0960 .
Med.all.antiplateletyes -0.2032226 0.1666655 -1.219 0.2235
GFR_MDRD -0.0029755 0.0026983 -1.103 0.2709
BMI -0.0159516 0.0129552 -1.231 0.2190
MedHx_CVDyes 0.0868259 0.0983943 0.882 0.3781
stenose50-70% -0.1794300 0.6959617 -0.258 0.7967
stenose70-90% 0.2555452 0.6549512 0.390 0.6966
stenose90-99% 0.1340792 0.6527678 0.205 0.8374
stenose100% (Occlusion) -0.6984350 0.8092434 -0.863 0.3887
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9033 on 363 degrees of freedom
Multiple R-squared: 0.1522, Adjusted R-squared: 0.1055
F-statistic: 3.26 on 20 and 363 DF, p-value: 4.293e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: TGFB_rank
Effect size...............: 0.030478
Standard error............: 0.048642
Odds ratio (effect size)..: 1.031
Lower 95% CI..............: 0.937
Upper 95% CI..............: 1.134
T-value...................: 0.626577
P-value...................: 0.5313304
R^2.......................: 0.152247
Adjusted r^2..............: 0.105539
Sample size of AE DB......: 2423
Sample size of model......: 384
Missing data %............: 84.15188
- processing MMP2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year + Med.Statin.LLD +
GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes GFR_MDRD
0.338098 0.301133 -0.251680 -0.777395 -0.735250 -0.193209 -0.164077 -0.004672
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.6803 -0.6070 -0.0518 0.5260 3.1645
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.8044531 0.9438476 0.852 0.39460
currentDF[, TRAIT] 0.0560660 0.0510532 1.098 0.27284
Age -0.0008547 0.0058626 -0.146 0.88417
Gendermale 0.3289459 0.1022646 3.217 0.00141 **
ORdate_year2003 -0.2687989 0.1592387 -1.688 0.09225 .
ORdate_year2004 -0.7543716 0.1531908 -4.924 1.28e-06 ***
ORdate_year2005 -0.7369381 0.1599290 -4.608 5.62e-06 ***
ORdate_year2006 -0.1674271 0.5424294 -0.309 0.75775
Hypertension.compositeyes -0.0126844 0.1436731 -0.088 0.92970
DiabetesStatusDiabetes 0.0463723 0.1160518 0.400 0.68970
SmokerStatusEx-smoker -0.0792441 0.1035977 -0.765 0.44481
SmokerStatusNever smoked 0.1075409 0.1600364 0.672 0.50202
Med.Statin.LLDyes -0.1730065 0.1062664 -1.628 0.10437
Med.all.antiplateletyes -0.2403679 0.1675267 -1.435 0.15219
GFR_MDRD -0.0041004 0.0026164 -1.567 0.11793
BMI -0.0170419 0.0129058 -1.320 0.18749
MedHx_CVDyes 0.0750652 0.0973810 0.771 0.44130
stenose50-70% -0.1072533 0.6899420 -0.155 0.87655
stenose70-90% 0.2737108 0.6504720 0.421 0.67416
stenose90-99% 0.1595966 0.6482223 0.246 0.80566
stenose100% (Occlusion) -0.6884949 0.8034646 -0.857 0.39205
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8974 on 368 degrees of freedom
Multiple R-squared: 0.1633, Adjusted R-squared: 0.1179
F-statistic: 3.592 on 20 and 368 DF, p-value: 5.192e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MMP2_rank
Effect size...............: 0.056066
Standard error............: 0.051053
Odds ratio (effect size)..: 1.058
Lower 95% CI..............: 0.957
Upper 95% CI..............: 1.169
T-value...................: 1.098188
P-value...................: 0.2728405
R^2.......................: 0.163326
Adjusted r^2..............: 0.117855
Sample size of AE DB......: 2423
Sample size of model......: 389
Missing data %............: 83.94552
- processing MMP8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + GFR_MDRD + BMI, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes GFR_MDRD
0.823385 0.216987 0.209736 -0.256595 -0.762826 -0.765846 0.055867 -0.151716 -0.003546
BMI
-0.019190
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.90430 -0.55041 -0.07377 0.44086 2.97765
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.4099114 0.9202754 1.532 0.1264
currentDF[, TRAIT] 0.2192458 0.0486402 4.507 8.83e-06 ***
Age -0.0009982 0.0056856 -0.176 0.8607
Gendermale 0.2239516 0.1014932 2.207 0.0280 *
ORdate_year2003 -0.2853285 0.1552681 -1.838 0.0669 .
ORdate_year2004 -0.7614742 0.1482292 -5.137 4.53e-07 ***
ORdate_year2005 -0.8014470 0.1557124 -5.147 4.32e-07 ***
ORdate_year2006 0.0235618 0.5301464 0.044 0.9646
Hypertension.compositeyes -0.0653939 0.1379105 -0.474 0.6357
DiabetesStatusDiabetes 0.1011562 0.1138536 0.888 0.3749
SmokerStatusEx-smoker -0.0428219 0.1009383 -0.424 0.6716
SmokerStatusNever smoked 0.1577907 0.1549460 1.018 0.3092
Med.Statin.LLDyes -0.1617359 0.1036411 -1.561 0.1195
Med.all.antiplateletyes -0.1789455 0.1633129 -1.096 0.2739
GFR_MDRD -0.0028857 0.0025612 -1.127 0.2606
BMI -0.0256475 0.0126898 -2.021 0.0440 *
MedHx_CVDyes 0.0692398 0.0948298 0.730 0.4658
stenose50-70% -0.5353545 0.6785781 -0.789 0.4307
stenose70-90% -0.1694195 0.6414181 -0.264 0.7918
stenose90-99% -0.2261302 0.6373611 -0.355 0.7229
stenose100% (Occlusion) -1.1926910 0.7912626 -1.507 0.1326
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.875 on 368 degrees of freedom
Multiple R-squared: 0.2045, Adjusted R-squared: 0.1613
F-statistic: 4.73 on 20 and 368 DF, p-value: 3.39e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MMP8_rank
Effect size...............: 0.219246
Standard error............: 0.04864
Odds ratio (effect size)..: 1.245
Lower 95% CI..............: 1.132
Upper 95% CI..............: 1.37
T-value...................: 4.5075
P-value...................: 8.827215e-06
R^2.......................: 0.204504
Adjusted r^2..............: 0.161271
Sample size of AE DB......: 2423
Sample size of model......: 389
Missing data %............: 83.94552
- processing MMP9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes GFR_MDRD
0.26923 0.11089 0.27291 -0.25455 -0.74076 -0.71291 -0.04879 -0.16067 -0.00380
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.75413 -0.59568 -0.08101 0.49023 2.99490
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.981648 0.931406 1.054 0.29260
currentDF[, TRAIT] 0.116487 0.046977 2.480 0.01360 *
Age -0.001322 0.005792 -0.228 0.81953
Gendermale 0.282758 0.102188 2.767 0.00594 **
ORdate_year2003 -0.274364 0.158155 -1.735 0.08362 .
ORdate_year2004 -0.739096 0.151687 -4.873 1.64e-06 ***
ORdate_year2005 -0.732502 0.158451 -4.623 5.25e-06 ***
ORdate_year2006 -0.041877 0.541732 -0.077 0.93843
Hypertension.compositeyes -0.016857 0.140730 -0.120 0.90472
DiabetesStatusDiabetes 0.070094 0.115758 0.606 0.54520
SmokerStatusEx-smoker -0.062740 0.102689 -0.611 0.54160
SmokerStatusNever smoked 0.116806 0.157807 0.740 0.45966
Med.Statin.LLDyes -0.177601 0.105544 -1.683 0.09328 .
Med.all.antiplateletyes -0.230107 0.166016 -1.386 0.16657
GFR_MDRD -0.003383 0.002618 -1.292 0.19708
BMI -0.021590 0.012897 -1.674 0.09499 .
MedHx_CVDyes 0.080841 0.096570 0.837 0.40307
stenose50-70% -0.173328 0.685380 -0.253 0.80049
stenose70-90% 0.201328 0.646574 0.311 0.75569
stenose90-99% 0.082991 0.644337 0.129 0.89759
stenose100% (Occlusion) -0.785658 0.799020 -0.983 0.32612
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8914 on 368 degrees of freedom
Multiple R-squared: 0.1744, Adjusted R-squared: 0.1295
F-statistic: 3.886 on 20 and 368 DF, p-value: 7.919e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MMP9_rank
Effect size...............: 0.116487
Standard error............: 0.046977
Odds ratio (effect size)..: 1.124
Lower 95% CI..............: 1.025
Upper 95% CI..............: 1.232
T-value...................: 2.479671
P-value...................: 0.01359724
R^2.......................: 0.174379
Adjusted r^2..............: 0.129509
Sample size of AE DB......: 2423
Sample size of model......: 389
Missing data %............: 83.94552
Analysis of MCP1_rank.
- processing IL2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.23530 -0.07964 0.21501 -0.15491 -0.55216 -0.37803 0.67483
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4049 -0.7012 -0.0038 0.5859 2.3840
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.848283 1.047428 1.765 0.078502 .
currentDF[, TRAIT] -0.101148 0.053804 -1.880 0.060946 .
Age -0.011740 0.006831 -1.719 0.086554 .
Gendermale 0.267209 0.121010 2.208 0.027879 *
ORdate_year2003 -0.184561 0.165265 -1.117 0.264864
ORdate_year2004 -0.545484 0.159587 -3.418 0.000705 ***
ORdate_year2005 -0.366003 0.187541 -1.952 0.051782 .
ORdate_year2006 0.518191 1.022887 0.507 0.612756
Hypertension.compositeyes -0.210624 0.155553 -1.354 0.176597
DiabetesStatusDiabetes -0.106744 0.137434 -0.777 0.437860
SmokerStatusEx-smoker 0.061633 0.119038 0.518 0.604953
SmokerStatusNever smoked 0.330852 0.180023 1.838 0.066933 .
Med.Statin.LLDyes -0.159744 0.118760 -1.345 0.179461
Med.all.antiplateletyes 0.252134 0.205162 1.229 0.219913
GFR_MDRD -0.002995 0.003074 -0.974 0.330633
BMI -0.006828 0.015322 -0.446 0.656138
MedHx_CVDyes 0.054650 0.110418 0.495 0.620957
stenose50-70% -0.466439 0.777976 -0.600 0.549190
stenose70-90% -0.478441 0.713241 -0.671 0.502790
stenose90-99% -0.506488 0.712093 -0.711 0.477392
stenose100% (Occlusion) -0.610468 0.885679 -0.689 0.491112
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.983 on 351 degrees of freedom
Multiple R-squared: 0.08883, Adjusted R-squared: 0.03691
F-statistic: 1.711 on 20 and 351 DF, p-value: 0.02987
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL2_rank
Effect size...............: -0.101148
Standard error............: 0.053804
Odds ratio (effect size)..: 0.904
Lower 95% CI..............: 0.813
Upper 95% CI..............: 1.004
T-value...................: -1.879925
P-value...................: 0.0609463
R^2.......................: 0.088833
Adjusted r^2..............: 0.036915
Sample size of AE DB......: 2423
Sample size of model......: 372
Missing data %............: 84.64713
- processing IL4_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005
0.10618 0.30893 -0.07914 -0.49442 -0.37080
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3931 -0.6475 0.0000 0.6287 2.4924
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.486476 1.301703 1.142 0.25432
currentDF[, TRAIT] -0.055618 0.057804 -0.962 0.33667
Age -0.014363 0.007283 -1.972 0.04945 *
Gendermale 0.354824 0.128867 2.753 0.00623 **
ORdate_year2003 -0.120201 0.170495 -0.705 0.48131
ORdate_year2004 -0.511441 0.164941 -3.101 0.00210 **
ORdate_year2005 -0.336604 0.210683 -1.598 0.11109
Hypertension.compositeyes -0.105245 0.165041 -0.638 0.52413
DiabetesStatusDiabetes -0.127010 0.144714 -0.878 0.38078
SmokerStatusEx-smoker 0.086406 0.124243 0.695 0.48727
SmokerStatusNever smoked 0.331539 0.189531 1.749 0.08120 .
Med.Statin.LLDyes -0.145743 0.126117 -1.156 0.24869
Med.all.antiplateletyes 0.153953 0.218802 0.704 0.48218
GFR_MDRD -0.004264 0.003483 -1.224 0.22168
BMI -0.001818 0.016438 -0.111 0.91198
MedHx_CVDyes 0.060857 0.116569 0.522 0.60198
stenose50-70% -0.078947 1.072694 -0.074 0.94138
stenose70-90% -0.121344 1.026800 -0.118 0.90600
stenose90-99% -0.145174 1.023649 -0.142 0.88731
stenose100% (Occlusion) -0.231865 1.157149 -0.200 0.84131
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9965 on 323 degrees of freedom
Multiple R-squared: 0.08632, Adjusted R-squared: 0.03257
F-statistic: 1.606 on 19 and 323 DF, p-value: 0.05288
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL4_rank
Effect size...............: -0.055618
Standard error............: 0.057804
Odds ratio (effect size)..: 0.946
Lower 95% CI..............: 0.845
Upper 95% CI..............: 1.059
T-value...................: -0.962188
P-value...................: 0.3366749
R^2.......................: 0.086319
Adjusted r^2..............: 0.032573
Sample size of AE DB......: 2423
Sample size of model......: 343
Missing data %............: 85.844
- processing IL5_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 Med.Statin.LLDyes
1.12214 -0.13824 -0.01092 0.29015 -0.25261 -0.57500 -0.37475 -0.21141
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5687 -0.6862 -0.0048 0.6209 2.3100
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.903143 1.252632 1.519 0.129602
currentDF[, TRAIT] -0.144817 0.055107 -2.628 0.008976 **
Age -0.016151 0.006981 -2.313 0.021289 *
Gendermale 0.344463 0.121217 2.842 0.004755 **
ORdate_year2003 -0.267518 0.167413 -1.598 0.110973
ORdate_year2004 -0.590545 0.161143 -3.665 0.000287 ***
ORdate_year2005 -0.366374 0.195788 -1.871 0.062155 .
Hypertension.compositeyes -0.098017 0.155401 -0.631 0.528633
DiabetesStatusDiabetes -0.144241 0.139146 -1.037 0.300644
SmokerStatusEx-smoker 0.054103 0.119212 0.454 0.650231
SmokerStatusNever smoked 0.298490 0.183945 1.623 0.105568
Med.Statin.LLDyes -0.227432 0.119616 -1.901 0.058093 .
Med.all.antiplateletyes 0.161901 0.210509 0.769 0.442366
GFR_MDRD -0.004499 0.003182 -1.414 0.158348
BMI -0.004071 0.015091 -0.270 0.787493
MedHx_CVDyes 0.098741 0.110851 0.891 0.373686
stenose50-70% -0.049307 1.053748 -0.047 0.962706
stenose70-90% -0.184517 1.007450 -0.183 0.854787
stenose90-99% -0.197154 1.005255 -0.196 0.844630
stenose100% (Occlusion) -0.379178 1.134974 -0.334 0.738519
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9809 on 344 degrees of freedom
Multiple R-squared: 0.1013, Adjusted R-squared: 0.05164
F-statistic: 2.04 on 19 and 344 DF, p-value: 0.006619
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL5_rank
Effect size...............: -0.144817
Standard error............: 0.055107
Odds ratio (effect size)..: 0.865
Lower 95% CI..............: 0.777
Upper 95% CI..............: 0.964
T-value...................: -2.62792
P-value...................: 0.008975765
R^2.......................: 0.101274
Adjusted r^2..............: 0.051635
Sample size of AE DB......: 2423
Sample size of model......: 364
Missing data %............: 84.9773
- processing IL6_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.2104 0.2316 -0.1363 -0.5066 -0.3522 0.8614
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2724 -0.6452 -0.0209 0.6350 2.4711
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.759403 0.976668 1.801 0.07247 .
currentDF[, TRAIT] 0.030563 0.054038 0.566 0.57204
Age -0.014444 0.007030 -2.055 0.04063 *
Gendermale 0.294443 0.121366 2.426 0.01575 *
ORdate_year2003 -0.133605 0.165694 -0.806 0.42058
ORdate_year2004 -0.469786 0.157871 -2.976 0.00312 **
ORdate_year2005 -0.334514 0.184009 -1.818 0.06990 .
ORdate_year2006 0.801630 1.027347 0.780 0.43573
Hypertension.compositeyes -0.143471 0.156798 -0.915 0.36080
DiabetesStatusDiabetes -0.077917 0.136994 -0.569 0.56987
SmokerStatusEx-smoker 0.101893 0.117760 0.865 0.38747
SmokerStatusNever smoked 0.351952 0.180921 1.945 0.05251 .
Med.Statin.LLDyes -0.157046 0.118196 -1.329 0.18479
Med.all.antiplateletyes 0.176871 0.191928 0.922 0.35738
GFR_MDRD -0.004154 0.003148 -1.319 0.18789
BMI -0.011718 0.014388 -0.814 0.41596
MedHx_CVDyes 0.048168 0.109822 0.439 0.66122
stenose50-70% -0.128799 0.655278 -0.197 0.84429
stenose70-90% -0.044582 0.596101 -0.075 0.94042
stenose90-99% -0.100930 0.594381 -0.170 0.86526
stenose100% (Occlusion) -0.235144 0.799025 -0.294 0.76871
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9961 on 361 degrees of freedom
Multiple R-squared: 0.0788, Adjusted R-squared: 0.02776
F-statistic: 1.544 on 20 and 361 DF, p-value: 0.06424
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL6_rank
Effect size...............: 0.030563
Standard error............: 0.054038
Odds ratio (effect size)..: 1.031
Lower 95% CI..............: 0.927
Upper 95% CI..............: 1.146
T-value...................: 0.56557
P-value...................: 0.5720374
R^2.......................: 0.078797
Adjusted r^2..............: 0.027761
Sample size of AE DB......: 2423
Sample size of model......: 382
Missing data %............: 84.23442
- processing IL8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.all.antiplatelet + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005
1.746047 0.320534 -0.017912 0.316057 -0.233243 -0.634237 -0.644911
ORdate_year2006 Hypertension.compositeyes DiabetesStatusDiabetes SmokerStatusEx-smoker SmokerStatusNever smoked Med.all.antiplateletyes GFR_MDRD
-0.597377 -0.241263 -0.191609 0.094128 0.377339 0.315498 -0.005266
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2966 -0.6066 -0.0329 0.6210 2.4949
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.277405 0.909206 2.505 0.012709 *
currentDF[, TRAIT] 0.320233 0.052086 6.148 2.15e-09 ***
Age -0.018894 0.006551 -2.884 0.004169 **
Gendermale 0.322043 0.114438 2.814 0.005170 **
ORdate_year2003 -0.231115 0.156692 -1.475 0.141131
ORdate_year2004 -0.614216 0.150277 -4.087 5.43e-05 ***
ORdate_year2005 -0.653710 0.173009 -3.778 0.000186 ***
ORdate_year2006 -0.558391 0.558774 -0.999 0.318339
Hypertension.compositeyes -0.213410 0.149883 -1.424 0.155391
DiabetesStatusDiabetes -0.162752 0.130152 -1.250 0.211969
SmokerStatusEx-smoker 0.104168 0.110792 0.940 0.347759
SmokerStatusNever smoked 0.415486 0.176193 2.358 0.018921 *
Med.Statin.LLDyes -0.098911 0.110817 -0.893 0.372708
Med.all.antiplateletyes 0.300771 0.176441 1.705 0.089154 .
GFR_MDRD -0.005270 0.002840 -1.856 0.064309 .
BMI -0.017022 0.013462 -1.264 0.206919
MedHx_CVDyes 0.028785 0.104263 0.276 0.782653
stenose50-70% 0.003125 0.617269 0.005 0.995963
stenose70-90% 0.061496 0.555425 0.111 0.911903
stenose90-99% -0.027594 0.555208 -0.050 0.960389
stenose100% (Occlusion) -0.629759 0.729712 -0.863 0.388720
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9295 on 347 degrees of freedom
Multiple R-squared: 0.1996, Adjusted R-squared: 0.1534
F-statistic: 4.326 on 20 and 347 DF, p-value: 5.356e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL8_rank
Effect size...............: 0.320233
Standard error............: 0.052086
Odds ratio (effect size)..: 1.377
Lower 95% CI..............: 1.244
Upper 95% CI..............: 1.525
T-value...................: 6.148217
P-value...................: 2.15447e-09
R^2.......................: 0.199579
Adjusted r^2..............: 0.153445
Sample size of AE DB......: 2423
Sample size of model......: 368
Missing data %............: 84.81222
- processing IL9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes
0.31212 0.27560 0.29723 0.05936 -0.60422 -0.54485 -0.91781 -0.13908
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.05144 -0.61291 -0.06995 0.60519 2.63870
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.334683 0.791831 1.686 0.092610 .
currentDF[, TRAIT] 0.268352 0.042406 6.328 6.28e-10 ***
Age -0.007407 0.005594 -1.324 0.186192
Gendermale 0.316749 0.096903 3.269 0.001168 **
ORdate_year2003 0.029965 0.146829 0.204 0.838389
ORdate_year2004 -0.591826 0.137004 -4.320 1.94e-05 ***
ORdate_year2005 -0.534007 0.137073 -3.896 0.000114 ***
ORdate_year2006 -0.891648 0.229144 -3.891 0.000116 ***
Hypertension.compositeyes -0.121691 0.129489 -0.940 0.347864
DiabetesStatusDiabetes -0.117500 0.110493 -1.063 0.288195
SmokerStatusEx-smoker 0.035250 0.096643 0.365 0.715480
SmokerStatusNever smoked 0.126922 0.139800 0.908 0.364451
Med.Statin.LLDyes -0.151524 0.100396 -1.509 0.131971
Med.all.antiplateletyes 0.066243 0.156609 0.423 0.672519
GFR_MDRD -0.001649 0.002380 -0.693 0.488885
BMI -0.009385 0.011390 -0.824 0.410419
MedHx_CVDyes 0.044254 0.090683 0.488 0.625794
stenose50-70% -0.300550 0.569894 -0.527 0.598204
stenose70-90% -0.149477 0.525154 -0.285 0.776061
stenose90-99% -0.154885 0.523596 -0.296 0.767519
stenose100% (Occlusion) -0.784568 0.664605 -1.181 0.238458
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8855 on 427 degrees of freedom
Multiple R-squared: 0.219, Adjusted R-squared: 0.1824
F-statistic: 5.985 on 20 and 427 DF, p-value: 5.172e-14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL9_rank
Effect size...............: 0.268352
Standard error............: 0.042406
Odds ratio (effect size)..: 1.308
Lower 95% CI..............: 1.204
Upper 95% CI..............: 1.421
T-value...................: 6.328117
P-value...................: 6.279561e-10
R^2.......................: 0.218953
Adjusted r^2..............: 0.182371
Sample size of AE DB......: 2423
Sample size of model......: 448
Missing data %............: 81.51052
- processing IL10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 Med.Statin.LLDyes GFR_MDRD
1.66777 -0.15368 -0.01527 0.34483 -0.16360 -0.56370 -0.22898 -0.23441 -0.00475
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4848 -0.6137 -0.0184 0.6148 2.2746
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.433177 1.323805 1.838 0.067032 .
currentDF[, TRAIT] -0.168926 0.060679 -2.784 0.005706 **
Age -0.018124 0.007485 -2.421 0.016042 *
Gendermale 0.377838 0.131800 2.867 0.004436 **
ORdate_year2003 -0.204996 0.174336 -1.176 0.240564
ORdate_year2004 -0.590085 0.169259 -3.486 0.000562 ***
ORdate_year2005 -0.259823 0.234085 -1.110 0.267894
Hypertension.compositeyes -0.165915 0.173430 -0.957 0.339494
DiabetesStatusDiabetes -0.071867 0.149057 -0.482 0.630048
SmokerStatusEx-smoker 0.072528 0.128165 0.566 0.571883
SmokerStatusNever smoked 0.333142 0.193268 1.724 0.085770 .
Med.Statin.LLDyes -0.243407 0.128434 -1.895 0.059012 .
Med.all.antiplateletyes 0.108766 0.234275 0.464 0.642788
GFR_MDRD -0.005019 0.003529 -1.422 0.156055
BMI -0.010762 0.016583 -0.649 0.516865
MedHx_CVDyes 0.031011 0.122242 0.254 0.799907
stenose50-70% -0.303753 1.079896 -0.281 0.778687
stenose70-90% -0.304863 1.027481 -0.297 0.766891
stenose90-99% -0.291901 1.025009 -0.285 0.776007
stenose100% (Occlusion) -0.824624 1.216891 -0.678 0.498508
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9955 on 305 degrees of freedom
Multiple R-squared: 0.113, Adjusted R-squared: 0.05771
F-statistic: 2.044 on 19 and 305 DF, p-value: 0.006742
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL10_rank
Effect size...............: -0.168926
Standard error............: 0.060679
Odds ratio (effect size)..: 0.845
Lower 95% CI..............: 0.75
Upper 95% CI..............: 0.951
T-value...................: -2.783917
P-value...................: 0.005706097
R^2.......................: 0.112963
Adjusted r^2..............: 0.057705
Sample size of AE DB......: 2423
Sample size of model......: 325
Missing data %............: 86.58688
- processing IL12_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 Med.Statin.LLDyes
0.25829 -0.10927 0.29482 -0.08404 -0.48430 -0.35556 -0.17322
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.6020 -0.6429 -0.0080 0.6231 2.3950
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.760489 1.299745 1.354 0.17653
currentDF[, TRAIT] -0.120052 0.057487 -2.088 0.03755 *
Age -0.014300 0.007311 -1.956 0.05133 .
Gendermale 0.377303 0.126183 2.990 0.00300 **
ORdate_year2003 -0.119471 0.171834 -0.695 0.48738
ORdate_year2004 -0.489444 0.163387 -2.996 0.00295 **
ORdate_year2005 -0.344317 0.214341 -1.606 0.10916
Hypertension.compositeyes -0.119065 0.165822 -0.718 0.47326
DiabetesStatusDiabetes -0.062871 0.142360 -0.442 0.65905
SmokerStatusEx-smoker 0.027143 0.125642 0.216 0.82909
SmokerStatusNever smoked 0.301946 0.186033 1.623 0.10555
Med.Statin.LLDyes -0.206909 0.126019 -1.642 0.10159
Med.all.antiplateletyes 0.190588 0.214277 0.889 0.37443
GFR_MDRD -0.004761 0.003391 -1.404 0.16128
BMI -0.007759 0.016332 -0.475 0.63506
MedHx_CVDyes 0.072183 0.116227 0.621 0.53501
stenose50-70% -0.166068 1.062755 -0.156 0.87592
stenose70-90% -0.172888 1.017062 -0.170 0.86513
stenose90-99% -0.201607 1.013750 -0.199 0.84249
stenose100% (Occlusion) -0.429050 1.176227 -0.365 0.71552
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9871 on 323 degrees of freedom
Multiple R-squared: 0.09763, Adjusted R-squared: 0.04455
F-statistic: 1.839 on 19 and 323 DF, p-value: 0.01817
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL12_rank
Effect size...............: -0.120052
Standard error............: 0.057487
Odds ratio (effect size)..: 0.887
Lower 95% CI..............: 0.792
Upper 95% CI..............: 0.993
T-value...................: -2.08834
P-value...................: 0.03754997
R^2.......................: 0.097629
Adjusted r^2..............: 0.044548
Sample size of AE DB......: 2423
Sample size of model......: 343
Missing data %............: 85.844
- processing IL13_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.7084 0.4117 0.2230 -0.4286 -0.8308 -0.7895 -1.0289
Hypertension.compositeyes Med.Statin.LLDyes
-0.1656 -0.1294
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.6322 -0.6262 -0.0719 0.6089 2.3823
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.411e+00 7.795e-01 1.810 0.07092 .
currentDF[, TRAIT] 4.054e-01 4.186e-02 9.685 < 2e-16 ***
Age -2.610e-03 5.344e-03 -0.488 0.62554
Gendermale 2.326e-01 9.269e-02 2.510 0.01242 *
ORdate_year2003 -4.337e-01 1.426e-01 -3.042 0.00248 **
ORdate_year2004 -8.206e-01 1.381e-01 -5.943 5.41e-09 ***
ORdate_year2005 -7.948e-01 1.398e-01 -5.685 2.29e-08 ***
ORdate_year2006 -1.012e+00 2.301e-01 -4.397 1.35e-05 ***
Hypertension.compositeyes -1.516e-01 1.217e-01 -1.246 0.21355
DiabetesStatusDiabetes -6.280e-02 1.031e-01 -0.609 0.54277
SmokerStatusEx-smoker 7.175e-03 9.192e-02 0.078 0.93781
SmokerStatusNever smoked 9.541e-02 1.364e-01 0.699 0.48458
Med.Statin.LLDyes -1.452e-01 9.512e-02 -1.526 0.12762
Med.all.antiplateletyes 3.044e-02 1.460e-01 0.209 0.83492
GFR_MDRD -1.927e-05 2.293e-03 -0.008 0.99330
BMI -1.158e-02 1.091e-02 -1.061 0.28914
MedHx_CVDyes 2.354e-02 8.590e-02 0.274 0.78415
stenose50-70% -3.405e-01 5.687e-01 -0.599 0.54959
stenose70-90% -2.424e-01 5.276e-01 -0.460 0.64607
stenose90-99% -2.839e-01 5.263e-01 -0.539 0.58980
stenose100% (Occlusion) -8.155e-01 6.675e-01 -1.222 0.22237
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8918 on 476 degrees of freedom
Multiple R-squared: 0.2518, Adjusted R-squared: 0.2204
F-statistic: 8.011 on 20 and 476 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL13_rank
Effect size...............: 0.40537
Standard error............: 0.041856
Odds ratio (effect size)..: 1.5
Lower 95% CI..............: 1.382
Upper 95% CI..............: 1.628
T-value...................: 9.684912
P-value...................: 2.286334e-20
R^2.......................: 0.251827
Adjusted r^2..............: 0.220391
Sample size of AE DB......: 2423
Sample size of model......: 497
Missing data %............: 79.48824
- processing IL21_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.6197 0.3659 0.2233 -0.3950 -0.7946 -0.7503 -1.0267
Hypertension.compositeyes
-0.2089
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5724 -0.6658 -0.0753 0.5936 2.3724
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.5847839 0.7987640 1.984 0.04782 *
currentDF[, TRAIT] 0.3604917 0.0428612 8.411 4.76e-16 ***
Age -0.0046079 0.0054566 -0.844 0.39884
Gendermale 0.2327429 0.0949004 2.452 0.01454 *
ORdate_year2003 -0.3932853 0.1451318 -2.710 0.00697 **
ORdate_year2004 -0.7656537 0.1408802 -5.435 8.77e-08 ***
ORdate_year2005 -0.7349384 0.1425936 -5.154 3.74e-07 ***
ORdate_year2006 -0.9650642 0.2357760 -4.093 5.00e-05 ***
Hypertension.compositeyes -0.1821238 0.1246139 -1.462 0.14454
DiabetesStatusDiabetes -0.0794686 0.1056597 -0.752 0.45235
SmokerStatusEx-smoker 0.0227465 0.0939926 0.242 0.80888
SmokerStatusNever smoked 0.1289001 0.1396774 0.923 0.35656
Med.Statin.LLDyes -0.1341216 0.0973676 -1.377 0.16901
Med.all.antiplateletyes 0.0275211 0.1497367 0.184 0.85425
GFR_MDRD -0.0001929 0.0023500 -0.082 0.93463
BMI -0.0132500 0.0111897 -1.184 0.23696
MedHx_CVDyes 0.0307600 0.0878726 0.350 0.72645
stenose50-70% -0.3868357 0.5828365 -0.664 0.50720
stenose70-90% -0.2602515 0.5408091 -0.481 0.63058
stenose90-99% -0.3053022 0.5395082 -0.566 0.57173
stenose100% (Occlusion) -0.9487593 0.6840086 -1.387 0.16607
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9142 on 477 degrees of freedom
Multiple R-squared: 0.2193, Adjusted R-squared: 0.1866
F-statistic: 6.7 on 20 and 477 DF, p-value: 2.546e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL21_rank
Effect size...............: 0.360492
Standard error............: 0.042861
Odds ratio (effect size)..: 1.434
Lower 95% CI..............: 1.318
Upper 95% CI..............: 1.56
T-value...................: 8.410682
P-value...................: 4.761059e-16
R^2.......................: 0.219306
Adjusted r^2..............: 0.186572
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing INFG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.1370 0.3631 -0.1473 -0.5341 -0.5197 -1.2411
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3447 -0.6496 0.0000 0.6491 2.6895
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.682312 0.977635 1.721 0.086181 .
currentDF[, TRAIT] -0.097580 0.057826 -1.687 0.092415 .
Age -0.012809 0.007004 -1.829 0.068310 .
Gendermale 0.372336 0.123069 3.025 0.002669 **
ORdate_year2003 -0.217345 0.167966 -1.294 0.196533
ORdate_year2004 -0.591412 0.161244 -3.668 0.000283 ***
ORdate_year2005 -0.593979 0.200030 -2.969 0.003192 **
ORdate_year2006 -1.600518 1.028025 -1.557 0.120412
Hypertension.compositeyes -0.036781 0.162348 -0.227 0.820902
DiabetesStatusDiabetes -0.059012 0.136010 -0.434 0.664644
SmokerStatusEx-smoker 0.159400 0.120064 1.328 0.185177
SmokerStatusNever smoked 0.354197 0.181567 1.951 0.051890 .
Med.Statin.LLDyes -0.178313 0.122510 -1.456 0.146437
Med.all.antiplateletyes 0.134618 0.195416 0.689 0.491362
GFR_MDRD -0.002009 0.003091 -0.650 0.516140
BMI -0.017970 0.014645 -1.227 0.220653
MedHx_CVDyes 0.071411 0.113773 0.628 0.530640
stenose50-70% 0.079320 0.673447 0.118 0.906309
stenose70-90% -0.188459 0.595379 -0.317 0.751787
stenose90-99% -0.111600 0.594907 -0.188 0.851307
stenose100% (Occlusion) -0.410901 0.844306 -0.487 0.626798
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9936 on 346 degrees of freedom
Multiple R-squared: 0.1001, Adjusted R-squared: 0.04812
F-statistic: 1.925 on 20 and 346 DF, p-value: 0.01035
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: INFG_rank
Effect size...............: -0.09758
Standard error............: 0.057826
Odds ratio (effect size)..: 0.907
Lower 95% CI..............: 0.81
Upper 95% CI..............: 1.016
T-value...................: -1.687465
P-value...................: 0.09241517
R^2.......................: 0.100138
Adjusted r^2..............: 0.048122
Sample size of AE DB......: 2423
Sample size of model......: 367
Missing data %............: 84.85349
- processing TNFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 Med.all.antiplateletyes
-0.29272 0.28620 -0.05321 -0.42616 -0.23496 0.37042
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3833 -0.6541 0.0224 0.5984 2.5513
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.670758 1.289679 1.295 0.19610
currentDF[, TRAIT] -0.024005 0.058343 -0.411 0.68102
Age -0.015070 0.007232 -2.084 0.03798 *
Gendermale 0.348552 0.127149 2.741 0.00647 **
ORdate_year2003 -0.074712 0.176557 -0.423 0.67247
ORdate_year2004 -0.415875 0.173406 -2.398 0.01705 *
ORdate_year2005 -0.219727 0.211493 -1.039 0.29963
Hypertension.compositeyes -0.112492 0.166351 -0.676 0.49939
DiabetesStatusDiabetes -0.098186 0.143790 -0.683 0.49520
SmokerStatusEx-smoker 0.057961 0.126002 0.460 0.64583
SmokerStatusNever smoked 0.396289 0.188102 2.107 0.03592 *
Med.Statin.LLDyes -0.185508 0.125345 -1.480 0.13987
Med.all.antiplateletyes 0.317760 0.224683 1.414 0.15827
GFR_MDRD -0.003597 0.003383 -1.063 0.28845
BMI -0.012674 0.016120 -0.786 0.43230
MedHx_CVDyes 0.029650 0.118697 0.250 0.80291
stenose50-70% -0.201872 1.064060 -0.190 0.84965
stenose70-90% -0.173288 1.018696 -0.170 0.86503
stenose90-99% -0.230479 1.017355 -0.227 0.82092
stenose100% (Occlusion) -0.750642 1.268893 -0.592 0.55456
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9906 on 316 degrees of freedom
Multiple R-squared: 0.09163, Adjusted R-squared: 0.03701
F-statistic: 1.678 on 19 and 316 DF, p-value: 0.03862
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: TNFA_rank
Effect size...............: -0.024005
Standard error............: 0.058343
Odds ratio (effect size)..: 0.976
Lower 95% CI..............: 0.871
Upper 95% CI..............: 1.095
T-value...................: -0.411449
P-value...................: 0.6810222
R^2.......................: 0.091631
Adjusted r^2..............: 0.037014
Sample size of AE DB......: 2423
Sample size of model......: 336
Missing data %............: 86.13289
- processing MIF_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale Med.Statin.LLDyes
-0.02938 0.39025 0.22422 -0.16262
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1820 -0.6122 -0.0160 0.6373 2.6385
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.106946 0.813735 1.360 0.17437
currentDF[, TRAIT] 0.368110 0.050375 7.307 1.15e-12 ***
Age -0.006395 0.005529 -1.157 0.24799
Gendermale 0.256464 0.096261 2.664 0.00798 **
ORdate_year2003 -0.202603 0.145424 -1.393 0.16421
ORdate_year2004 -0.190072 0.148023 -1.284 0.19974
ORdate_year2005 -0.220313 0.150646 -1.462 0.14427
ORdate_year2006 -0.512689 0.247465 -2.072 0.03882 *
Hypertension.compositeyes -0.161706 0.126770 -1.276 0.20272
DiabetesStatusDiabetes -0.006809 0.107850 -0.063 0.94969
SmokerStatusEx-smoker 0.149665 0.095272 1.571 0.11686
SmokerStatusNever smoked 0.321505 0.140927 2.281 0.02297 *
Med.Statin.LLDyes -0.147579 0.098960 -1.491 0.13654
Med.all.antiplateletyes 0.070711 0.151873 0.466 0.64172
GFR_MDRD 0.001537 0.002408 0.638 0.52357
BMI -0.014754 0.011380 -1.296 0.19544
MedHx_CVDyes 0.011720 0.089326 0.131 0.89567
stenose50-70% -0.419061 0.592269 -0.708 0.47957
stenose70-90% -0.269334 0.549588 -0.490 0.62431
stenose90-99% -0.330280 0.548328 -0.602 0.54723
stenose100% (Occlusion) -0.879931 0.695157 -1.266 0.20620
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.929 on 477 degrees of freedom
Multiple R-squared: 0.1938, Adjusted R-squared: 0.16
F-statistic: 5.733 on 20 and 477 DF, p-value: 1.843e-13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MIF_rank
Effect size...............: 0.36811
Standard error............: 0.050375
Odds ratio (effect size)..: 1.445
Lower 95% CI..............: 1.309
Upper 95% CI..............: 1.595
T-value...................: 7.307443
P-value...................: 1.153927e-12
R^2.......................: 0.193782
Adjusted r^2..............: 0.159978
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing MCP1_rankattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsense
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] GFR_MDRD
1.493e-17 1.000e+00 -2.237e-19 essentially perfect fit: summary may be unreliable
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.517e-16 -2.374e-17 -1.570e-18 1.826e-17 5.424e-16
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.950e-18 4.886e-17 2.040e-01 0.839
currentDF[, TRAIT] 1.000e+00 2.605e-18 3.839e+17 <2e-16 ***
Age 1.203e-19 3.316e-19 3.630e-01 0.717
Gendermale 3.821e-18 5.814e-18 6.570e-01 0.511
ORdate_year2003 1.896e-18 8.738e-18 2.170e-01 0.828
ORdate_year2004 -5.142e-18 8.536e-18 -6.020e-01 0.547
ORdate_year2005 -3.734e-18 8.722e-18 -4.280e-01 0.669
ORdate_year2006 8.825e-18 1.459e-17 6.050e-01 0.546
Hypertension.compositeyes 1.104e-18 7.614e-18 1.450e-01 0.885
DiabetesStatusDiabetes 3.394e-18 6.445e-18 5.270e-01 0.599
SmokerStatusEx-smoker -2.418e-18 5.708e-18 -4.240e-01 0.672
SmokerStatusNever smoked 3.067e-18 8.478e-18 3.620e-01 0.718
Med.Statin.LLDyes -2.693e-18 5.947e-18 -4.530e-01 0.651
Med.all.antiplateletyes -2.875e-18 9.104e-18 -3.160e-01 0.752
GFR_MDRD -2.044e-19 1.432e-19 -1.427e+00 0.154
BMI 4.587e-19 6.826e-19 6.720e-01 0.502
MedHx_CVDyes 1.970e-18 5.358e-18 3.680e-01 0.713
stenose50-70% -7.133e-18 3.555e-17 -2.010e-01 0.841
stenose70-90% -1.351e-17 3.298e-17 -4.100e-01 0.682
stenose90-99% -1.225e-17 3.290e-17 -3.720e-01 0.710
stenose100% (Occlusion) 2.041e-17 4.178e-17 4.890e-01 0.625
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 5.573e-17 on 477 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 8.219e+33 on 20 and 477 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MCP1_rank ' .essentially perfect fit: summary may be unreliableCollecting data.essentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableWe have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MCP1_rank
Effect size...............: 1
Standard error............: 0
Odds ratio (effect size)..: 2.718
Lower 95% CI..............: 2.718
Upper 95% CI..............: 2.718
T-value...................: 3.838704e+17
P-value...................: 0
R^2.......................: 1
Adjusted r^2..............: 1
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing MIP1a_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + DiabetesStatus, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 DiabetesStatusDiabetes
0.34170 0.34792 0.21872 0.01273 -0.67674 -0.62317 -0.90498 -0.14902
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.85964 -0.62199 -0.05546 0.53987 2.80125
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.511020 0.767382 1.969 0.04958 *
currentDF[, TRAIT] 0.341430 0.041926 8.144 4.03e-15 ***
Age -0.006624 0.005397 -1.227 0.22036
Gendermale 0.242232 0.094257 2.570 0.01050 *
ORdate_year2003 -0.011709 0.140843 -0.083 0.93378
ORdate_year2004 -0.654808 0.134143 -4.881 1.48e-06 ***
ORdate_year2005 -0.616772 0.134140 -4.598 5.59e-06 ***
ORdate_year2006 -0.856247 0.223927 -3.824 0.00015 ***
Hypertension.compositeyes -0.088229 0.123983 -0.712 0.47708
DiabetesStatusDiabetes -0.122595 0.104651 -1.171 0.24205
SmokerStatusEx-smoker -0.005287 0.093389 -0.057 0.95488
SmokerStatusNever smoked 0.125724 0.135395 0.929 0.35362
Med.Statin.LLDyes -0.123126 0.096924 -1.270 0.20464
Med.all.antiplateletyes 0.066835 0.151188 0.442 0.65866
GFR_MDRD -0.000337 0.002290 -0.147 0.88307
BMI -0.012508 0.010904 -1.147 0.25194
MedHx_CVDyes 0.033628 0.087530 0.384 0.70102
stenose50-70% -0.438874 0.556519 -0.789 0.43077
stenose70-90% -0.294956 0.513095 -0.575 0.56568
stenose90-99% -0.337677 0.511544 -0.660 0.50953
stenose100% (Occlusion) -0.960749 0.649425 -1.479 0.13976
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8658 on 438 degrees of freedom
Multiple R-squared: 0.2531, Adjusted R-squared: 0.219
F-statistic: 7.421 on 20 and 438 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.34143
Standard error............: 0.041926
Odds ratio (effect size)..: 1.407
Lower 95% CI..............: 1.296
Upper 95% CI..............: 1.527
T-value...................: 8.143573
P-value...................: 4.034538e-15
R^2.......................: 0.253098
Adjusted r^2..............: 0.218993
Sample size of AE DB......: 2423
Sample size of model......: 459
Missing data %............: 81.05654
- processing RANTES_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.13754 0.32235 0.23131 0.08187 -0.11140 -0.19089 -0.61898
Hypertension.compositeyes
-0.21297
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1775 -0.5601 -0.0099 0.5696 2.8566
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.0783090 0.8122665 1.328 0.1850
currentDF[, TRAIT] 0.3333263 0.0481504 6.923 1.46e-11 ***
Age -0.0034700 0.0055582 -0.624 0.5327
Gendermale 0.2312370 0.0965463 2.395 0.0170 *
ORdate_year2003 0.0736781 0.1490460 0.494 0.6213
ORdate_year2004 -0.0746579 0.1539889 -0.485 0.6280
ORdate_year2005 -0.1708068 0.1528205 -1.118 0.2643
ORdate_year2006 -0.5664307 0.2449618 -2.312 0.0212 *
Hypertension.compositeyes -0.2180770 0.1274686 -1.711 0.0878 .
DiabetesStatusDiabetes 0.0184105 0.1080754 0.170 0.8648
SmokerStatusEx-smoker 0.1228629 0.0951180 1.292 0.1971
SmokerStatusNever smoked 0.2132704 0.1406225 1.517 0.1300
Med.Statin.LLDyes -0.1352462 0.0992172 -1.363 0.1735
Med.all.antiplateletyes 0.1222422 0.1527526 0.800 0.4240
GFR_MDRD 0.0002519 0.0023819 0.106 0.9158
BMI -0.0198699 0.0114090 -1.742 0.0822 .
MedHx_CVDyes 0.0018433 0.0896710 0.021 0.9836
stenose50-70% -0.4821863 0.5917325 -0.815 0.4156
stenose70-90% -0.3390314 0.5469091 -0.620 0.5356
stenose90-99% -0.2765726 0.5453633 -0.507 0.6123
stenose100% (Occlusion) -1.2176208 0.6925501 -1.758 0.0794 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.924 on 471 degrees of freedom
Multiple R-squared: 0.1922, Adjusted R-squared: 0.1579
F-statistic: 5.605 on 20 and 471 DF, p-value: 4.604e-13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: RANTES_rank
Effect size...............: 0.333326
Standard error............: 0.04815
Odds ratio (effect size)..: 1.396
Lower 95% CI..............: 1.27
Upper 95% CI..............: 1.534
T-value...................: 6.922604
P-value...................: 1.457728e-11
R^2.......................: 0.192244
Adjusted r^2..............: 0.157944
Sample size of AE DB......: 2423
Sample size of model......: 492
Missing data %............: 79.69459
- processing MIG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.6147 0.3149 0.2421 -0.2002 -0.8325 -0.8245 -1.2520
Hypertension.compositeyes
-0.2097
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2989 -0.6538 -0.0206 0.6585 2.4880
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.7187707 0.8125689 2.115 0.03494 *
currentDF[, TRAIT] 0.3067410 0.0461474 6.647 8.36e-11 ***
Age -0.0053480 0.0055964 -0.956 0.33976
Gendermale 0.2561059 0.0968292 2.645 0.00845 **
ORdate_year2003 -0.2091149 0.1463437 -1.429 0.15369
ORdate_year2004 -0.8049399 0.1456358 -5.527 5.42e-08 ***
ORdate_year2005 -0.8062573 0.1474557 -5.468 7.44e-08 ***
ORdate_year2006 -1.1887282 0.2411509 -4.929 1.15e-06 ***
Hypertension.compositeyes -0.1875140 0.1284114 -1.460 0.14489
DiabetesStatusDiabetes -0.0565490 0.1082308 -0.522 0.60158
SmokerStatusEx-smoker -0.0030809 0.0961023 -0.032 0.97444
SmokerStatusNever smoked 0.1233658 0.1439433 0.857 0.39186
Med.Statin.LLDyes -0.1306656 0.0995996 -1.312 0.19020
Med.all.antiplateletyes 0.0098681 0.1535225 0.064 0.94878
GFR_MDRD -0.0004724 0.0023902 -0.198 0.84343
BMI -0.0135743 0.0114144 -1.189 0.23496
MedHx_CVDyes 0.0146332 0.0902740 0.162 0.87130
stenose50-70% -0.4022242 0.5928882 -0.678 0.49784
stenose70-90% -0.3192665 0.5480168 -0.583 0.56045
stenose90-99% -0.3069092 0.5464577 -0.562 0.57463
stenose100% (Occlusion) -1.0298434 0.6932430 -1.486 0.13807
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9258 on 467 degrees of freedom
Multiple R-squared: 0.1894, Adjusted R-squared: 0.1547
F-statistic: 5.455 on 20 and 467 DF, p-value: 1.317e-12
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MIG_rank
Effect size...............: 0.306741
Standard error............: 0.046147
Odds ratio (effect size)..: 1.359
Lower 95% CI..............: 1.241
Upper 95% CI..............: 1.488
T-value...................: 6.646979
P-value...................: 8.364781e-11
R^2.......................: 0.189367
Adjusted r^2..............: 0.154651
Sample size of AE DB......: 2423
Sample size of model......: 488
Missing data %............: 79.85968
- processing IP10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + BMI, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 BMI
0.77283 0.43800 0.27132 -0.24330 -0.72066 -0.66445 -0.88073 -0.01686
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1935 -0.6181 -0.0426 0.5849 2.1062
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.645850 0.781301 2.107 0.035742 *
currentDF[, TRAIT] 0.436243 0.042900 10.169 < 2e-16 ***
Age -0.005519 0.005497 -1.004 0.315962
Gendermale 0.289862 0.093381 3.104 0.002036 **
ORdate_year2003 -0.257289 0.141328 -1.821 0.069385 .
ORdate_year2004 -0.695342 0.133731 -5.200 3.11e-07 ***
ORdate_year2005 -0.619589 0.134966 -4.591 5.83e-06 ***
ORdate_year2006 -0.816434 0.236651 -3.450 0.000617 ***
Hypertension.compositeyes -0.159754 0.124365 -1.285 0.199647
DiabetesStatusDiabetes -0.062656 0.105363 -0.595 0.552382
SmokerStatusEx-smoker -0.042598 0.094790 -0.449 0.653374
SmokerStatusNever smoked 0.004468 0.141013 0.032 0.974736
Med.Statin.LLDyes -0.131231 0.097117 -1.351 0.177331
Med.all.antiplateletyes 0.012678 0.146104 0.087 0.930893
GFR_MDRD -0.002136 0.002341 -0.912 0.362069
BMI -0.015856 0.011047 -1.435 0.151915
MedHx_CVDyes 0.067512 0.087381 0.773 0.440179
stenose50-70% -0.255101 0.551942 -0.462 0.644183
stenose70-90% -0.249418 0.510319 -0.489 0.625272
stenose90-99% -0.155738 0.508469 -0.306 0.759535
stenose100% (Occlusion) -0.627892 0.645832 -0.972 0.331493
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8597 on 425 degrees of freedom
Multiple R-squared: 0.291, Adjusted R-squared: 0.2576
F-statistic: 8.72 on 20 and 425 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IP10_rank
Effect size...............: 0.436243
Standard error............: 0.0429
Odds ratio (effect size)..: 1.547
Lower 95% CI..............: 1.422
Upper 95% CI..............: 1.683
T-value...................: 10.16884
P-value...................: 6.913888e-22
R^2.......................: 0.290961
Adjusted r^2..............: 0.257595
Sample size of AE DB......: 2423
Sample size of model......: 446
Missing data %............: 81.59307
- processing Eotaxin1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.6058 0.3432 0.2024 -0.2701 -0.7809 -0.7411 -1.0666
Hypertension.compositeyes
-0.2147
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4611 -0.6645 -0.0691 0.6036 2.5756
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.643e+00 8.077e-01 2.034 0.0425 *
currentDF[, TRAIT] 3.371e-01 4.396e-02 7.668 9.91e-14 ***
Age -5.604e-03 5.509e-03 -1.017 0.3096
Gendermale 2.131e-01 9.636e-02 2.211 0.0275 *
ORdate_year2003 -2.723e-01 1.451e-01 -1.877 0.0611 .
ORdate_year2004 -7.544e-01 1.426e-01 -5.289 1.87e-07 ***
ORdate_year2005 -7.302e-01 1.444e-01 -5.058 6.04e-07 ***
ORdate_year2006 -1.011e+00 2.385e-01 -4.239 2.70e-05 ***
Hypertension.compositeyes -1.869e-01 1.260e-01 -1.484 0.1385
DiabetesStatusDiabetes -8.350e-02 1.068e-01 -0.782 0.4348
SmokerStatusEx-smoker 3.041e-02 9.502e-02 0.320 0.7491
SmokerStatusNever smoked 1.421e-01 1.412e-01 1.007 0.3146
Med.Statin.LLDyes -1.185e-01 9.847e-02 -1.203 0.2294
Med.all.antiplateletyes 2.258e-02 1.515e-01 0.149 0.8816
GFR_MDRD -3.441e-05 2.377e-03 -0.014 0.9885
BMI -1.295e-02 1.131e-02 -1.145 0.2530
MedHx_CVDyes 2.726e-02 8.885e-02 0.307 0.7591
stenose50-70% -4.056e-01 5.893e-01 -0.688 0.4916
stenose70-90% -2.916e-01 5.468e-01 -0.533 0.5941
stenose90-99% -3.383e-01 5.456e-01 -0.620 0.5354
stenose100% (Occlusion) -1.012e+00 6.916e-01 -1.463 0.1441
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9243 on 477 degrees of freedom
Multiple R-squared: 0.2019, Adjusted R-squared: 0.1684
F-statistic: 6.033 on 20 and 477 DF, p-value: 2.367e-14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.337065
Standard error............: 0.04396
Odds ratio (effect size)..: 1.401
Lower 95% CI..............: 1.285
Upper 95% CI..............: 1.527
T-value...................: 7.667585
P-value...................: 9.914825e-14
R^2.......................: 0.201897
Adjusted r^2..............: 0.168434
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing TARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Med.Statin.LLD + BMI, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006 Med.Statin.LLDyes BMI
0.64458 0.28156 0.23852 -0.08327 -0.03539 -0.43743 -0.25518 -0.01889
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.93209 -0.61908 -0.02249 0.59434 2.56625
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.197723 0.864304 1.386 0.1666
currentDF[, TRAIT] 0.266485 0.047864 5.568 4.7e-08 ***
Age -0.004423 0.005922 -0.747 0.4556
Gendermale 0.129835 0.103988 1.249 0.2125
ORdate_year2003 0.163787 0.224440 0.730 0.4660
ORdate_year2004 -0.116781 0.221758 -0.527 0.5987
ORdate_year2005 -0.081089 0.225417 -0.360 0.7192
ORdate_year2006 -0.478714 0.295835 -1.618 0.1064
Hypertension.compositeyes -0.177075 0.138719 -1.276 0.2025
DiabetesStatusDiabetes -0.059647 0.116006 -0.514 0.6074
SmokerStatusEx-smoker 0.026575 0.103800 0.256 0.7981
SmokerStatusNever smoked 0.182409 0.148154 1.231 0.2190
Med.Statin.LLDyes -0.258100 0.109955 -2.347 0.0194 *
Med.all.antiplateletyes 0.040805 0.161971 0.252 0.8012
GFR_MDRD 0.000504 0.002645 0.191 0.8490
BMI -0.020111 0.012493 -1.610 0.1082
MedHx_CVDyes 0.092574 0.097619 0.948 0.3435
stenose50-70% -0.493106 0.599490 -0.823 0.4113
stenose70-90% -0.283985 0.551917 -0.515 0.6072
stenose90-99% -0.244692 0.550143 -0.445 0.6567
stenose100% (Occlusion) -0.895351 0.699517 -1.280 0.2013
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9295 on 406 degrees of freedom
Multiple R-squared: 0.1586, Adjusted R-squared: 0.1171
F-statistic: 3.826 on 20 and 406 DF, p-value: 9.858e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: TARC_rank
Effect size...............: 0.266485
Standard error............: 0.047864
Odds ratio (effect size)..: 1.305
Lower 95% CI..............: 1.188
Upper 95% CI..............: 1.434
T-value...................: 5.567569
P-value...................: 4.704389e-08
R^2.......................: 0.158581
Adjusted r^2..............: 0.117132
Sample size of AE DB......: 2423
Sample size of model......: 427
Missing data %............: 82.37722
- processing PARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + Med.Statin.LLD + Med.all.antiplatelet, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale Med.Statin.LLDyes Med.all.antiplateletyes
0.140410 0.468193 -0.007411 0.287539 -0.164246 0.301974
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.96205 -0.57951 -0.01007 0.61114 2.19833
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.443166 0.784019 0.565 0.572170
currentDF[, TRAIT] 0.454454 0.044922 10.116 < 2e-16 ***
Age -0.007448 0.005277 -1.411 0.158765
Gendermale 0.305702 0.091803 3.330 0.000936 ***
ORdate_year2003 -0.146890 0.139199 -1.055 0.291841
ORdate_year2004 -0.072548 0.141826 -0.512 0.609219
ORdate_year2005 -0.201222 0.141637 -1.421 0.156060
ORdate_year2006 -0.524023 0.233362 -2.246 0.025191 *
Hypertension.compositeyes -0.144044 0.121304 -1.187 0.235635
DiabetesStatusDiabetes -0.018525 0.102922 -0.180 0.857239
SmokerStatusEx-smoker 0.077139 0.090971 0.848 0.396888
SmokerStatusNever smoked 0.176671 0.135090 1.308 0.191570
Med.Statin.LLDyes -0.128540 0.094677 -1.358 0.175210
Med.all.antiplateletyes 0.266120 0.145703 1.826 0.068408 .
GFR_MDRD 0.001331 0.002294 0.580 0.561879
BMI -0.005762 0.010888 -0.529 0.596929
MedHx_CVDyes 0.051227 0.085471 0.599 0.549224
stenose50-70% -0.089648 0.567672 -0.158 0.874585
stenose70-90% -0.066489 0.526127 -0.126 0.899489
stenose90-99% -0.079401 0.524811 -0.151 0.879807
stenose100% (Occlusion) -0.248353 0.668644 -0.371 0.710484
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8889 on 477 degrees of freedom
Multiple R-squared: 0.2619, Adjusted R-squared: 0.2309
F-statistic: 8.462 on 20 and 477 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: PARC_rank
Effect size...............: 0.454454
Standard error............: 0.044922
Odds ratio (effect size)..: 1.575
Lower 95% CI..............: 1.443
Upper 95% CI..............: 1.72
T-value...................: 10.11645
P-value...................: 6.328314e-22
R^2.......................: 0.261893
Adjusted r^2..............: 0.230945
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing MDC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
-0.01559 0.36766 0.30995 0.15211 -0.30987 -0.27047 -0.66058
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.9222 -0.6158 -0.0825 0.5485 3.1944
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.0346578 0.7727390 1.339 0.181290
currentDF[, TRAIT] 0.3644206 0.0437787 8.324 1.12e-15 ***
Age -0.0056229 0.0053771 -1.046 0.296278
Gendermale 0.3280538 0.0944454 3.473 0.000565 ***
ORdate_year2003 0.1322027 0.1437472 0.920 0.358248
ORdate_year2004 -0.2824810 0.1381149 -2.045 0.041433 *
ORdate_year2005 -0.2473922 0.1397622 -1.770 0.077415 .
ORdate_year2006 -0.5861314 0.2295243 -2.554 0.011001 *
Hypertension.compositeyes -0.0970933 0.1252900 -0.775 0.438793
DiabetesStatusDiabetes -0.0851312 0.1064360 -0.800 0.424245
SmokerStatusEx-smoker 0.0043359 0.0935495 0.046 0.963054
SmokerStatusNever smoked 0.1191810 0.1359298 0.877 0.381090
Med.Statin.LLDyes -0.1427309 0.0978155 -1.459 0.145239
Med.all.antiplateletyes 0.1179179 0.1535521 0.768 0.442945
GFR_MDRD -0.0001572 0.0023234 -0.068 0.946097
BMI -0.0134623 0.0109893 -1.225 0.221226
MedHx_CVDyes 0.0444548 0.0881999 0.504 0.614501
stenose50-70% -0.3709802 0.5593856 -0.663 0.507560
stenose70-90% -0.2545538 0.5156477 -0.494 0.621798
stenose90-99% -0.2871570 0.5141252 -0.559 0.576768
stenose100% (Occlusion) -0.9118922 0.6524890 -1.398 0.162961
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8699 on 433 degrees of freedom
Multiple R-squared: 0.2539, Adjusted R-squared: 0.2195
F-statistic: 7.368 on 20 and 433 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MDC_rank
Effect size...............: 0.364421
Standard error............: 0.043779
Odds ratio (effect size)..: 1.44
Lower 95% CI..............: 1.321
Upper 95% CI..............: 1.569
T-value...................: 8.324156
P-value...................: 1.122615e-15
R^2.......................: 0.253914
Adjusted r^2..............: 0.219453
Sample size of AE DB......: 2423
Sample size of model......: 454
Missing data %............: 81.2629
- processing OPG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.4320 0.5815 0.1814 -0.1449 -0.3454 -0.5624 -1.0233
Hypertension.compositeyes Med.Statin.LLDyes
-0.1470 -0.1268
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5340 -0.5103 -0.0607 0.4740 2.5929
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.7798576 0.6963882 1.120 0.26334
currentDF[, TRAIT] 0.5769753 0.0371733 15.521 < 2e-16 ***
Age -0.0024880 0.0047478 -0.524 0.60050
Gendermale 0.1970550 0.0826726 2.384 0.01754 *
ORdate_year2003 -0.1515277 0.1247232 -1.215 0.22500
ORdate_year2004 -0.3456070 0.1207390 -2.862 0.00439 **
ORdate_year2005 -0.5659112 0.1226870 -4.613 5.12e-06 ***
ORdate_year2006 -1.0090680 0.2050977 -4.920 1.19e-06 ***
Hypertension.compositeyes -0.1566629 0.1084312 -1.445 0.14917
DiabetesStatusDiabetes -0.0209632 0.0919990 -0.228 0.81985
SmokerStatusEx-smoker -0.0057051 0.0817397 -0.070 0.94439
SmokerStatusNever smoked 0.0966923 0.1210565 0.799 0.42484
Med.Statin.LLDyes -0.1416772 0.0847985 -1.671 0.09543 .
Med.all.antiplateletyes 0.0046067 0.1300679 0.035 0.97176
GFR_MDRD -0.0008946 0.0020431 -0.438 0.66170
BMI -0.0046626 0.0097374 -0.479 0.63228
MedHx_CVDyes 0.0615065 0.0766321 0.803 0.42260
stenose50-70% -0.0828490 0.5073929 -0.163 0.87036
stenose70-90% -0.0157327 0.4706070 -0.033 0.97335
stenose90-99% -0.0377996 0.4693986 -0.081 0.93585
stenose100% (Occlusion) -0.3261846 0.5962811 -0.547 0.58461
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7951 on 476 degrees of freedom
Multiple R-squared: 0.4054, Adjusted R-squared: 0.3804
F-statistic: 16.22 on 20 and 476 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: OPG_rank
Effect size...............: 0.576975
Standard error............: 0.037173
Odds ratio (effect size)..: 1.781
Lower 95% CI..............: 1.656
Upper 95% CI..............: 1.915
T-value...................: 15.52122
P-value...................: 2.938028e-44
R^2.......................: 0.405353
Adjusted r^2..............: 0.380368
Sample size of AE DB......: 2423
Sample size of model......: 497
Missing data %............: 79.48824
- processing sICAM1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
Hypertension.composite + Med.Statin.LLD + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale Hypertension.compositeyes Med.Statin.LLDyes Med.all.antiplateletyes
-0.1133 0.6670 0.2354 -0.1616 -0.1266 0.1884
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.93159 -0.40951 0.02282 0.44011 2.10027
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.541408 0.656090 0.825 0.4097
currentDF[, TRAIT] 0.675007 0.036594 18.446 <2e-16 ***
Age 0.002150 0.004486 0.479 0.6320
Gendermale 0.230177 0.077424 2.973 0.0031 **
ORdate_year2003 -0.278792 0.117246 -2.378 0.0178 *
ORdate_year2004 -0.137463 0.115109 -1.194 0.2330
ORdate_year2005 -0.232101 0.116827 -1.987 0.0475 *
ORdate_year2006 -0.332592 0.196045 -1.697 0.0904 .
Hypertension.compositeyes -0.155632 0.102023 -1.525 0.1278
DiabetesStatusDiabetes -0.026329 0.086551 -0.304 0.7611
SmokerStatusEx-smoker 0.093427 0.076568 1.220 0.2230
SmokerStatusNever smoked 0.109994 0.113793 0.967 0.3342
Med.Statin.LLDyes -0.108080 0.079726 -1.356 0.1759
Med.all.antiplateletyes 0.182233 0.122196 1.491 0.1365
GFR_MDRD 0.001764 0.001928 0.915 0.3606
BMI -0.014141 0.009159 -1.544 0.1233
MedHx_CVDyes -0.035832 0.072023 -0.498 0.6191
stenose50-70% -0.529627 0.477171 -1.110 0.2676
stenose70-90% -0.347215 0.442780 -0.784 0.4333
stenose90-99% -0.428805 0.441755 -0.971 0.3322
stenose100% (Occlusion) -0.730640 0.560100 -1.304 0.1927
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7484 on 477 degrees of freedom
Multiple R-squared: 0.4768, Adjusted R-squared: 0.4548
F-statistic: 21.73 on 20 and 477 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: sICAM1_rank
Effect size...............: 0.675007
Standard error............: 0.036594
Odds ratio (effect size)..: 1.964
Lower 95% CI..............: 1.828
Upper 95% CI..............: 2.11
T-value...................: 18.44574
P-value...................: 9.595196e-58
R^2.......................: 0.476758
Adjusted r^2..............: 0.454819
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing VEGFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite + SmokerStatus + BMI,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
1.26611 0.34011 0.21423 -0.54641 -0.74319 -1.12291 -1.41133
Hypertension.compositeyes SmokerStatusEx-smoker SmokerStatusNever smoked BMI
-0.26383 0.10672 0.30832 -0.01872
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1971 -0.6033 -0.0341 0.6103 2.4464
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.426102 0.932754 2.601 0.00966 **
currentDF[, TRAIT] 0.347103 0.052723 6.584 1.52e-10 ***
Age -0.009494 0.006021 -1.577 0.11566
Gendermale 0.212048 0.105733 2.006 0.04561 *
ORdate_year2003 -0.563377 0.174245 -3.233 0.00133 **
ORdate_year2004 -0.735039 0.164002 -4.482 9.79e-06 ***
ORdate_year2005 -1.118244 0.185642 -6.024 4.00e-09 ***
ORdate_year2006 -1.374207 0.266390 -5.159 4.00e-07 ***
Hypertension.compositeyes -0.235011 0.139861 -1.680 0.09371 .
DiabetesStatusDiabetes -0.113451 0.117439 -0.966 0.33463
SmokerStatusEx-smoker 0.155532 0.102932 1.511 0.13161
SmokerStatusNever smoked 0.379226 0.156366 2.425 0.01576 *
Med.Statin.LLDyes -0.085997 0.107412 -0.801 0.42384
Med.all.antiplateletyes 0.126683 0.156012 0.812 0.41729
GFR_MDRD -0.001108 0.002533 -0.437 0.66219
BMI -0.022001 0.012678 -1.735 0.08349 .
MedHx_CVDyes 0.126802 0.097267 1.304 0.19314
stenose50-70% -0.367979 0.707504 -0.520 0.60329
stenose70-90% -0.483438 0.657580 -0.735 0.46268
stenose90-99% -0.544524 0.656574 -0.829 0.40743
stenose100% (Occlusion) -1.353700 0.777606 -1.741 0.08251 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9099 on 383 degrees of freedom
Multiple R-squared: 0.2047, Adjusted R-squared: 0.1632
F-statistic: 4.93 on 20 and 383 DF, p-value: 8.193e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: VEGFA_rank
Effect size...............: 0.347103
Standard error............: 0.052723
Odds ratio (effect size)..: 1.415
Lower 95% CI..............: 1.276
Upper 95% CI..............: 1.569
T-value...................: 6.583513
P-value...................: 1.516294e-10
R^2.......................: 0.204737
Adjusted r^2..............: 0.163209
Sample size of AE DB......: 2423
Sample size of model......: 404
Missing data %............: 83.32645
- processing TGFB_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.3610 0.1063 0.3122 -0.1580 -0.5054 -0.5324 -0.9055
Hypertension.compositeyes
-0.1955
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4274 -0.6350 0.0144 0.6228 2.5063
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.4138364 0.8539604 1.656 0.098491 .
currentDF[, TRAIT] 0.1052884 0.0464515 2.267 0.023884 *
Age -0.0076726 0.0058897 -1.303 0.193336
Gendermale 0.3375507 0.1030054 3.277 0.001130 **
ORdate_year2003 -0.1775071 0.1542223 -1.151 0.250347
ORdate_year2004 -0.4798233 0.1513448 -3.170 0.001626 **
ORdate_year2005 -0.5211650 0.1523953 -3.420 0.000683 ***
ORdate_year2006 -0.8380484 0.2668015 -3.141 0.001793 **
Hypertension.compositeyes -0.2042918 0.1341711 -1.523 0.128551
DiabetesStatusDiabetes -0.0788904 0.1153959 -0.684 0.494546
SmokerStatusEx-smoker 0.0918515 0.1012455 0.907 0.364775
SmokerStatusNever smoked 0.3292334 0.1516521 2.171 0.030451 *
Med.Statin.LLDyes -0.1420226 0.1059380 -1.341 0.180716
Med.all.antiplateletyes 0.1269841 0.1602690 0.792 0.428590
GFR_MDRD -0.0002776 0.0025554 -0.109 0.913539
BMI -0.0156628 0.0121368 -1.291 0.197529
MedHx_CVDyes 0.0746852 0.0950853 0.785 0.432597
stenose50-70% -0.4637323 0.6175244 -0.751 0.453070
stenose70-90% -0.2491616 0.5728465 -0.435 0.663803
stenose90-99% -0.2275925 0.5714373 -0.398 0.690611
stenose100% (Occlusion) -1.0065603 0.7245935 -1.389 0.165473
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9674 on 453 degrees of freedom
Multiple R-squared: 0.1205, Adjusted R-squared: 0.08172
F-statistic: 3.105 on 20 and 453 DF, p-value: 9.036e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: TGFB_rank
Effect size...............: 0.105288
Standard error............: 0.046452
Odds ratio (effect size)..: 1.111
Lower 95% CI..............: 1.014
Upper 95% CI..............: 1.217
T-value...................: 2.266629
P-value...................: 0.02388367
R^2.......................: 0.120549
Adjusted r^2..............: 0.081722
Sample size of AE DB......: 2423
Sample size of model......: 474
Missing data %............: 80.43747
- processing MMP2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005 ORdate_year2006
0.1026 0.3694 0.3707 -0.1823 -0.4201 -0.4531 -0.6073
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4715 -0.5619 -0.0070 0.5989 2.4890
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.8536811 0.8251478 1.035 0.30141
currentDF[, TRAIT] 0.3525292 0.0472242 7.465 4.26e-13 ***
Age -0.0078047 0.0056175 -1.389 0.16540
Gendermale 0.3960223 0.0977553 4.051 5.99e-05 ***
ORdate_year2003 -0.2065914 0.1446782 -1.428 0.15399
ORdate_year2004 -0.4056659 0.1407575 -2.882 0.00414 **
ORdate_year2005 -0.4390377 0.1440445 -3.048 0.00244 **
ORdate_year2006 -0.5844152 0.4366270 -1.338 0.18141
Hypertension.compositeyes -0.0969689 0.1302786 -0.744 0.45707
DiabetesStatusDiabetes -0.0142610 0.1100266 -0.130 0.89693
SmokerStatusEx-smoker 0.0092934 0.0968874 0.096 0.92363
SmokerStatusNever smoked 0.1614083 0.1434174 1.125 0.26099
Med.Statin.LLDyes -0.0994556 0.0991388 -1.003 0.31630
Med.all.antiplateletyes 0.1352402 0.1563510 0.865 0.38751
GFR_MDRD -0.0008966 0.0024642 -0.364 0.71615
BMI -0.0112171 0.0117404 -0.955 0.33987
MedHx_CVDyes 0.0296270 0.0909592 0.326 0.74479
stenose50-70% 0.0284986 0.5930939 0.048 0.96170
stenose70-90% 0.1040823 0.5488315 0.190 0.84967
stenose90-99% 0.1198609 0.5475368 0.219 0.82682
stenose100% (Occlusion) -0.5974547 0.6933538 -0.862 0.38931
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9239 on 456 degrees of freedom
Multiple R-squared: 0.1977, Adjusted R-squared: 0.1625
F-statistic: 5.619 on 20 and 456 DF, p-value: 4.683e-13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MMP2_rank
Effect size...............: 0.352529
Standard error............: 0.047224
Odds ratio (effect size)..: 1.423
Lower 95% CI..............: 1.297
Upper 95% CI..............: 1.561
T-value...................: 7.465003
P-value...................: 4.262372e-13
R^2.......................: 0.197722
Adjusted r^2..............: 0.162534
Sample size of AE DB......: 2423
Sample size of model......: 477
Missing data %............: 80.31366
- processing MMP8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + SmokerStatus +
Med.all.antiplatelet + BMI + stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005
2.46084 0.47151 -0.01068 0.13674 -0.19553 -0.50329 -0.60335
ORdate_year2006 Hypertension.compositeyes SmokerStatusEx-smoker SmokerStatusNever smoked Med.all.antiplateletyes BMI stenose50-70%
-0.53710 -0.29184 0.17410 0.32151 0.24257 -0.03184 -0.79791
stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.68813 -0.61330 -1.59376
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.10342 -0.51128 0.03165 0.57214 2.72279
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.455345 0.765657 3.207 0.001436 **
currentDF[, TRAIT] 0.471891 0.041330 11.418 < 2e-16 ***
Age -0.010843 0.005231 -2.073 0.038733 *
Gendermale 0.128803 0.092671 1.390 0.165240
ORdate_year2003 -0.184990 0.135155 -1.369 0.171760
ORdate_year2004 -0.484165 0.130563 -3.708 0.000234 ***
ORdate_year2005 -0.585265 0.134123 -4.364 1.58e-05 ***
ORdate_year2006 -0.500318 0.407937 -1.226 0.220659
Hypertension.compositeyes -0.271501 0.119928 -2.264 0.024051 *
DiabetesStatusDiabetes -0.025456 0.102695 -0.248 0.804342
SmokerStatusEx-smoker 0.178175 0.090398 1.971 0.049327 *
SmokerStatusNever smoked 0.320787 0.132951 2.413 0.016223 *
Med.Statin.LLDyes -0.090827 0.092605 -0.981 0.327211
Med.all.antiplateletyes 0.244687 0.145835 1.678 0.094065 .
GFR_MDRD 0.001043 0.002313 0.451 0.652414
BMI -0.031707 0.011050 -2.869 0.004303 **
MedHx_CVDyes 0.022930 0.084954 0.270 0.787354
stenose50-70% -0.844857 0.552703 -1.529 0.127059
stenose70-90% -0.718111 0.513407 -1.399 0.162578
stenose90-99% -0.650489 0.511274 -1.272 0.203918
stenose100% (Occlusion) -1.642782 0.650275 -2.526 0.011865 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8631 on 456 degrees of freedom
Multiple R-squared: 0.2998, Adjusted R-squared: 0.2691
F-statistic: 9.764 on 20 and 456 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MMP8_rank
Effect size...............: 0.471891
Standard error............: 0.04133
Odds ratio (effect size)..: 1.603
Lower 95% CI..............: 1.478
Upper 95% CI..............: 1.738
T-value...................: 11.4175
P-value...................: 9.956493e-27
R^2.......................: 0.299837
Adjusted r^2..............: 0.269128
Sample size of AE DB......: 2423
Sample size of model......: 477
Missing data %............: 80.31366
- processing MMP9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + SmokerStatus +
Med.all.antiplatelet + BMI, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year2003 ORdate_year2004 ORdate_year2005
1.53139 0.59999 -0.01150 0.21089 -0.18315 -0.35564 -0.42003
ORdate_year2006 Hypertension.compositeyes SmokerStatusEx-smoker SmokerStatusNever smoked Med.all.antiplateletyes BMI
-0.24594 -0.18514 0.12961 0.23101 0.32137 -0.03072
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.06008 -0.49021 -0.02165 0.49263 2.80095
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.816909 0.689787 2.634 0.008726 **
currentDF[, TRAIT] 0.603435 0.037428 16.122 < 2e-16 ***
Age -0.011370 0.004727 -2.405 0.016565 *
Gendermale 0.199907 0.082649 2.419 0.015965 *
ORdate_year2003 -0.183710 0.122131 -1.504 0.133223
ORdate_year2004 -0.332481 0.118625 -2.803 0.005283 **
ORdate_year2005 -0.415965 0.121339 -3.428 0.000663 ***
ORdate_year2006 -0.207004 0.369664 -0.560 0.575770
Hypertension.compositeyes -0.166847 0.109513 -1.524 0.128319
DiabetesStatusDiabetes 0.009552 0.092853 0.103 0.918114
SmokerStatusEx-smoker 0.137637 0.081517 1.688 0.092012 .
SmokerStatusNever smoked 0.242214 0.120225 2.015 0.044527 *
Med.Statin.LLDyes -0.107640 0.083676 -1.286 0.198960
Med.all.antiplateletyes 0.273044 0.131809 2.072 0.038874 *
GFR_MDRD 0.001616 0.002089 0.773 0.439701
BMI -0.033533 0.009969 -3.364 0.000834 ***
MedHx_CVDyes 0.044822 0.076756 0.584 0.559544
stenose50-70% -0.305655 0.498261 -0.613 0.539890
stenose70-90% -0.242633 0.462061 -0.525 0.599762
stenose90-99% -0.264472 0.460706 -0.574 0.566212
stenose100% (Occlusion) -1.035057 0.584515 -1.771 0.077264 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7799 on 455 degrees of freedom
Multiple R-squared: 0.4281, Adjusted R-squared: 0.403
F-statistic: 17.03 on 20 and 455 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MMP9_rank
Effect size...............: 0.603435
Standard error............: 0.037428
Odds ratio (effect size)..: 1.828
Lower 95% CI..............: 1.699
Upper 95% CI..............: 1.968
T-value...................: 16.12239
P-value...................: 1.390543e-46
R^2.......................: 0.428125
Adjusted r^2..............: 0.402988
Sample size of AE DB......: 2423
Sample size of model......: 476
Missing data %............: 80.35493 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)DT::datatable(GLM.results)
# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.MCP1_Plaque.Cytokines_Plaques.RANK.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
MCP1 cytokines plaque levels vs. vulnerability index
Here we calculate the plaque instability/vulnerability index
# Plaque vulnerability
table(AEDB.CEA$Macrophages.bin)
no/minor moderate/heavy
847 992 table(AEDB.CEA$Fat.bin_10)
<10% >10%
542 1316 table(AEDB.CEA$Collagen.bin)
no/minor moderate/heavy
382 1469 table(AEDB.CEA$SMC.bin)
no/minor moderate/heavy
602 1244 table(AEDB.CEA$IPH.bin)
no yes
746 1108 # SPSS code
#
# *** syntax- Plaque vulnerability**.
# COMPUTE Macro_instab = -999.
# IF macrophages.bin=2 Macro_instab=1.
# IF macrophages.bin=1 Macro_instab=0.
# EXECUTE.
#
# COMPUTE Fat10_instab = -999.
# IF Fat.bin_10=2 Fat10_instab=1.
# IF Fat.bin_10=1 Fat10_instab=0.
# EXECUTE.
#
# COMPUTE coll_instab=-999.
# IF Collagen.bin=2 coll_instab=0.
# IF Collagen.bin=1 coll_instab=1.
# EXECUTE.
#
#
# COMPUTE SMC_instab=-999.
# IF SMC.bin=2 SMC_instab=0.
# IF SMC.bin=1 SMC_instab=1.
# EXECUTE.
#
# COMPUTE IPH_instab=-999.
# IF IPH.bin=0 IPH_instab=0.
# IF IPH.bin=1 IPH_instab=1.
# EXECUTE.
#
# COMPUTE Instability=Macro_instab + Fat10_instab + coll_instab + SMC_instab + IPH_instab.
# EXECUTE.
# Fix plaquephenotypes
attach(AEDB.CEA)The following objects are masked from AEDB.CEA (pos = 3):
ABI_70, ABI_max, ABI_mean, ABI_min, ABI_OP, ablock, ablock2, ablock3, aceinhib, aceinhib2, acetylsa, Adiponectin_ng_ml_2015, Adiponectin_pg_ug_2015,
AE_AAA_bijzonderheden, Age, Age_Q, AgeGroup, AgeGroupSex, AgeSQR, aid, AlcoholUse, Aldosteron_recode, alg10201, alg10202, alg10203, alg10204, alg10205, alg105, alg106,
alg109, alg110, alg113, alg114, alg115, ALOX5, analg2, analg3, analgeti, Ang2, angioii, ANGPT2, anti_apoA1_IgG, anti_apoA1_index, anti_apoA1_na, antiall, antiall2,
antiarrh, antiarrh2, ANXA2, AP_Dx, AP_Dx1, AP_Dx2, APOB, artercon, Artery_summary, arteryop, AsymptSympt, AsymptSympt2G, bblock, bblock2, blocko, blocksnr, BMI, BMI_US,
BMI_WHO, BMI30ormore, brain401, brain402, brain403, brain404, brain405, brain406, brain407, brain408, brain409, brain410, brain411, brain412, brain413, brn40701,
bspoed, CAD_Dx, CAD_Dx1, CAD_Dx2, CAD_history, CADPAOD_history, Calc.bin, calcification, CalcificationPlaque, calcium, calcium2, calreg, carbasal, cardioembolic,
Caspase3_7, CAV1, CD44, CD44V3, CEA_or_CAS, CEL, CFD_recalc, cholverl, cholverl2, cholverl3, CI_history, clau1, clau2, Claudication, clopidog, CML, COL_Instability,
collagen, Collagen.bin, CollagenPlaque, combi1, combi2, combi3, comorbidity.DM, concablo, concablo2, concablo3, concace2, concacei, concacet, concalle, concanal,
concanal2, concanal3, concangi, concanta2, concanti, concanti2, concbblo, concbblo2, conccalc, conccalc2, conccalreg, conccarb, concchol, concchol2, concchol3,
concclau1, concclau2, concclop, conccom1, conccom2, conccom3, conccort, conccorthorm2, concderm, concdig, concdig2, concdig3, concdig4, concdipy, concdiur, concdiur2,
concdiur3, concerec, conceye, concgluc, concgluc2, concgluc3, concgluc4, concgrel, concinsu, conciron, conciron2, concneur, concneur2, concneur3, concneur4, concnitr,
concnitr2, concotant, concotcor, concoth2, concothe, concpros, concpsy5, concren, concresp, concrheu, concrheu2, concrheu3, concsta2, concstat, concthro, concthyr,
concthyr2, concvit2, concvita, Contralateral_surgery, conwhen, corticos, cortihorm2, creat, crp_all, CRP_avg, CRP_dif, crp_source, CRP_var, CST3_pg_ug,
CST3_serum_luminex, CTGF, cTNI_plasma, CTSA, CTSB, CTSL1, CTSS, cyr61, date_ic_patient, date_ic_researcher, Date.of.birth, date.previous.operation, date1yr, date3mon,
dateapprox_latest, dateapprox_worst, dateapprox1, dateapprox2, dateapprox3, dateapprox4, dateend1, dateend2, dateend3, dateend4, dateend5, dateend6, dateexact_latest,
dateexact_worst, dateexact1, dateexact2, dateexact3, dateexact4, dateok, dermacor, DiabetesStatus, diastoli, diet801, diet802, diet803, diet804, diet805, diet806,
diet807, diet808, diet809, diet810, diet811, diet812, diet813, diet814, diet815, diet816, diet817, diet818, diet819, diet820, diet821, diet822, diet823, diet824,
dipyridi, diuretic, diuretic2, diuretic3, DM, DM.composite, duaalantiplatelet, duplend, eaindexl, eaindexr, eCigarettes, edaplaqu_recalc, edavrspl, EGR, EMMPRIN_45kD,
EMMPRIN_58kD, ENDOGLIN, endpoint1, endpoint2, endpoint3, endpoint4, endpoint5, endpoint6, Eotaxin1, Eotaxin1_rank, EP_CAD, ep_cad_t_30days, ep_cad_t_3years,
EP_CAD_time, ep_cad.30days, EP_CI, ep_ci_t_30days, ep_ci_t_3years, EP_CI_time, ep_com_t_30days, ep_com_t_3years, EP_composite, EP_composite_time, EP_coronary,
ep_coronary_t_30days, ep_coronary_t_3years, ep_coronary_t_90days, EP_coronary_time, EP_CVdeath, ep_cvdeath_t_30days, ep_cvdeath_t_3years, ep_cvdeath_t_90days,
EP_CVdeath_time, EP_death, ep_death_t_30days, ep_death_t_3years, EP_death_time, EP_fatalCVA, ep_fatalCVA_t_30days, ep_fatalCVA_t_3years, EP_fatalCVA_time,
EP_hemorrhagic_stroke, ep_hemorrhagic_stroke_t_3years, EP_hemorrhagic_stroke_time, ep_hemorrhagic_stroke.3years, EP_ischemic_stroke, ep_ischemic_stroke_t_3years,
EP_ischemic_stroke_time, ep_ischemic_stroke.3years, EP_leg_amputation, EP_leg_amputation_time, ep_legamputation_t_30days, ep_legamputation_t_3years, EP_major,
ep_major_t_30days, ep_major_t_3years, ep_major_t_90days, EP_major_time, EP_MI, ep_mi_t_30days, ep_mi_t_3years, EP_MI_time, EP_nonstroke_event, EP_nonstroke_event_time,
ep_nonstroke_t_3years, EP_peripheral, ep_peripheral_t_30days, ep_peripheral_t_3years, EP_peripheral_time, EP_pta, ep_pta_t_30days, ep_pta_t_3years, EP_pta_time,
EP_stroke, ep_stroke_t_30days, ep_stroke_t_3years, ep_stroke_t_90days, EP_stroke_time, EP_strokeCVdeath, ep_strokeCVdeath_t_30days, ep_strokeCVdeath_t_3years,
EP_strokeCVdeath_time, EP_strokedeath, ep_strokedeath_t_30days, ep_strokedeath_t_3years, EP_strokedeath_time, ePackYearsSmoking, epcad.3years, epci.30days, epci.3years,
epcom.30days, epcom.3years, epcoronary.30days, epcoronary.3years, epcoronary.90days, epcvdeath.30days, epcvdeath.3years, epcvdeath.90days, epdeath.30days,
epdeath.3years, epfatalCVA.30days, epfatalCVA.3years, eplegamputation.30days, eplegamputation.3years, epmajor.30days, epmajor.3years, epmajor.90days, epmi.30days,
epmi.3years, epnonstroke.3years, epperipheral.30days, epperipheral.3years, eppta.30days, eppta.3years, epstroke.30days, epstroke.3years, epstroke.90days,
epstrokeCVdeath.30days, epstrokeCVdeath.3years, epstrokedeath.30days, epstrokedeath.3years, erec, Estradiol, everstroke_composite, Everstroke_Ipsilateral, exer901,
exer902, exer903, exer904, exer905, exer906, exer9071, exer9072, exer9073, exer9074, exer9075, exer9076, exer908, exer909, exer910, eyedrop, EZis, FABP_serum, FABP4,
FABP4_pg_ug, FABP4_serum_luminex, fat, Fat.bin_10, Fat.bin_40, FAT10_Instability, Fat10Perc, Femoral.interv, FH_AAA_broth, FH_AAA_comp, FH_AAA_mat, FH_AAA_parent,
FH_AAA_pat, FH_AAA_sibling, FH_AAA_sis, FH_amp_broth, FH_amp_comp, FH_amp_mat, FH_amp_parent, FH_amp_pat, FH_amp_sibling, FH_amp_sis, FH_CAD_broth, FH_CAD_comp,
FH_CAD_mat, FH_CAD_parent, FH_CAD_pat, FH_CAD_sibling, FH_CAD_sis, FH_corcalc_broth, FH_corcalc_comp, FH_corcalc_mat, FH_corcalc_parent, FH_corcalc_pat,
FH_corcalc_sibling, FH_corcalc_sis, FH_CVD_broth, FH_CVD_comp, FH_CVD_mat, FH_CVD_parent, FH_CVD_pat, FH_CVD_sibling, FH_CVD_sis, FH_CVdeath_broth, FH_CVdeath_comp,
FH_CVdeath_mat, FH_CVdeath_parent, FH_CVdeath_pat, FH_CVdeath_sibling, FH_CVdeath_sis, FH_DM_broth, FH_DM_comp, FH_DM_mat, FH_DM_parent, FH_DM_pat, FH_DM_sibling,
FH_DM_sis, FH_HC_broth, FH_HC_comp, FH_HC_mat, FH_HC_parent, FH_HC_pat, FH_HC_sibling, FH_HC_sis, FH_HT_broth, FH_HT_comp, FH_HT_mat, FH_HT_parent, FH_HT_pat,
FH_HT_sibling, FH_HT_sis, FH_MI_broth, FH_MI_comp, FH_MI_mat, FH_MI_parent, FH_MI_pat, FH_MI_sibling, FH_MI_sis, FH_otherCVD_broth, FH_otherCVD_comp, FH_otherCVD_mat,
FH_otherCVD_parent, FH_otherCVD_pat, FH_otherCVD_sibling, FH_otherCVD_sis, FH_PAD_broth, FH_PAD_comp, FH_PAD_mat, FH_PAD_parent, FH_PAD_pat, FH_PAD_sibling, FH_PAD_sis,
FH_PAV_broth, FH_PAV_comp, FH_PAV_mat, FH_PAV_parent, FH_PAV_pat, FH_PAV_sibling, FH_PAV_sis, FH_POB_broth, FH_POB_comp, FH_POB_mat, FH_POB_parent, FH_POB_pat,
FH_POB_sibling, FH_POB_sis, FH_risk_broth, FH_risk_comp, FH_risk_mat, FH_risk_parent, FH_risk_pat, FH_risk_sibling, FH_risk_sis, FH_Stroke_broth, FH_Stroke_comp,
FH_Stroke_mat, FH_Stroke_parent, FH_Stroke_pat, FH_Stroke_sibling, FH_Stroke_sis, FH_tromb_broth, FH_tromb_comp, FH_tromb_mat, FH_tromb_parent, FH_tromb_pat,
FH_tromb_sibling, FH_tromb_sis, filter_$, folicaci, followup1, followup2, followup3, Fontaine, FU_check, FU_check_date, FU.cutt.off.30days, FU.cutt.off.3years,
FU.cutt.off.90days, FU1JAAR, FU2JAAR, FU3JAAR, FURIN_low, FURIN_up, GDF15_plasma, geen_med, Gender, GFR_CG, GFR_MDRD, glucose, GR_Segment, GrB_plaque, GrB_serum, grel,
GrK_plaque, GrK_serum, GrM_plaque, GrM_serum, HA, hb, HDAC9, HDL, HDL_2016, HDL_all, HDL_avg, HDL_clinic, HDL_dif, HDL_final, HDL_finalCU, hdl_source, HDL_var,
heart300, heart301, heart302, heart303, heart304, heart305, heart306, heart307, heart308, heart309, heart310, heart311, heart312, heart313, heart314, heart315,
heart316, heart317, heart318, heart319, heart320, heart321, heart322, heart323, heart324, heart325, heart326, heart327, heart328, HIF1A, ho1, homocys, Hospital,
hrt31301, hsCRP_plasma, ht, HYAL55KD, HYALURON, Hypertension.composite, Hypertension.drugs, Hypertension.selfreport, Hypertension.selfreportdrug, Hypertension1,
Hypertension2, IL1_Beta, IL10, IL10_rank, IL12, IL12_rank, IL13, IL13_rank, IL17, IL2, IL2_rank, IL21, IL21_rank, IL4, IL4_rank, IL5, IL5_rank, IL6, IL6_pg_ml_2015,
IL6_pg_ug_2015, IL6_rank, IL6R_pg_ml_2015, IL6R_pg_ug_2015, IL8, IL8_pg_ml_2015, IL8_pg_ug_2015, IL8_rank, IL9, IL9_rank, indexsymptoms_latest, indexsymptoms_latest_4g,
indexsymptoms_worst, indexsymptoms_worst_4g, INFG, INFG_rank, informedconsent, insulin, insuline, INVULDAT, IP10, IP10_rank, IPH, IPH_extended.bin, IPH_Instability,
IPH.bin, ironfoli, ironfoli2, KDOQI, latest, LDL, LDL_2016, LDL_all, LDL_avg, LDL_clinic, LDL_dif, LDL_final, LDL_finalCU, ldl_source, LDL_var, leg501, leg502, leg503,
leg504, leg505, leg506, leg507, leg508, leg509, leg510, leg511, leg512, leg513, leg514, leg515, leg516, leg517, leg518, leg519, leg520, LMW1STME, LTB4, LTB4R,
MAC_binned, MAC_grouped, MAC_Instability, macmean0, macrophages, Macrophages_LN, macrophages_location, Macrophages_rank, Macrophages.bin, MAP, Mast_cells_plaque,
max.followup, MCP1, MCP1_LN, MCP1_pg_ml_2015, MCP1_pg_ml_2015_LN, MCP1_pg_ml_2015_rank, MCP1_pg_ug_2015, MCP1_pg_ug_2015_LN, MCP1_pg_ug_2015_rank, MCP1_rank,
MCSF_pg_ml_2015, MCSF_pg_ug_2015, MDC, MDC_rank, Med_notes, Med.ablock, Med.ACE_inh, Med.acetylsal, Med.acetylsal_Combi1, Med.acetylsal_Combi2, Med.acetylsal_Combi3,
Med.ADPinh, Med.all.antiplatelet, Med.angiot2.antag, Med.antiarrh, Med.anticoagulants, Med.ascal, Med.aspirin.derived, Med.bblocker, Med.calc_antag, Med.dipyridamole,
Med.diuretic, Med.LLD, Med.nitrate, Med.otheranthyp, Med.renin, Med.statin, Med.statin.derived, Med.Statin.LLD, Med.statin2, MedHx_CVD, media, MG_H1, MI_Dx, MI_Dx1,
MI_Dx2, MIF, MIF_rank, MIG, MIG_rank, MIP1a, MIP1a_rank, miRNA100_RNU19, miRNA100_RNU48, miRNA155_RNU19, miRNA155_RNU48, MMP14, MMP2, MMP2_rank, MMP2TIMP2, MMP8,
MMP8_rank, MMP9, MMP9_rank, MMP9TIMP1, MPO_plasma, MRP_14, MRP_8, MRP_8_14C, MRP_8_14C_buhlmann, MRP14_plasma, MRP8_14C_plasma, MRP8_plasma, negatibl, neuropsy,
neuropsy2, neuropsy3, neuropsy4, neurpsy5, neutrophils, NGAL, NGAL_low, NGAL_MMP9_complex, NGAL_MMP9_local, NGAL_MMP9_peripheral, NGAL_total, NGAL_up, nitrate,
nitrate2, NOD1, NOD2, nogobt1_recalculated, NTproBNP_plasma, Number_Events_Sorter, Number_Sorted_CD14, Number_Sorted_CD20, Number_Sorted_CD4_Cells,
Number_Sorted_CD8_Cells, oac701, oac702, oac70305, oac704, oac705, oac706, oac707, oac708, oac709, oac710, oac711, oac712, oac713, oac714, OKyear, OPG, OPG_plasma,
OPG_rank, OPN, OPN_2013, OPN_plasma, OR_blood, Oral.glucose.inh, oralgluc, oralgluc2, oralgluc3, oralgluc4, ORdate_epoch, ORdate_year, ORyear, ORyearGroup, othanthyp,
othcoron, other, other2, OverallPlaquePhenotype, PAI1_pg_ml_2015, PAI1_pg_ug_2015, PAOD, PARC, PARC_rank, patch, PCSK9_plasma, PDGF_BB_plasma, Percentage_CD14,
Percentage_CD20, Percentage_CD4, Percentage_CD8, Peripheral.interv, PKC, PLA2_plasma, Plaque_Vulnerability_Index, plaquephenotype, positibl, PrimaryLast, PrimaryLast1,
prostagl, PulsePressure, qual01, qual02, qual0301, qual0302, qual0303, qual0304, qual0305, qual0306, qual0307, qual0308, qual0309, qual0310, qual0401, qual0402,
qual0403, qual0404, qual0501, qual0502, qual0503, qual06, qual07, qual08, qual0901, qual0902, qual0903, qual0904, qual0905, qual0906, qual0907, qual0908, qual0909,
qual1010, qual1101, qual1102, qual1103, qual1104, RAAS_med, RANTES, RANTES_pg_ml_2015, RANTES_pg_ug_2015, RANTES_plasma, RANTES_rank, Ras, RE50_01, RE70_01,
Renine_recode, renineinh, restenos, restenosisOK, rheuma, rheuma2, rheuma3, risk601, risk602, risk603, risk604, risk605, risk606, risk607, risk608, risk609, risk610,
risk611, risk612, risk613, risk614, risk615, risk616, risk617, risk618, risk619, risk620, Segment_isolated_Tris_2015, SHBG, sICAM1, sICAM1_rank, SMAD1_5_8, SMAD2,
SMAD3, smc, SMC_binned, SMC_grouped, SMC_Instability, SMC_LN, smc_location, smc_macrophages_ratio, SMC_rank, SMC.bin, smcmean0, SmokerCurrent, SmokerStatus,
SmokingReported, SmokingYearOR, stat3P, statin2, statines, ste3mext, sten1yr, sten3mo, stenose, stenosis_con_bin, Stenosis_contralateral, Stenosis_ipsilateral,
Stroke_Dx, Stroke_eitherside, Stroke_history, Stroke_Symptoms, StrokeTIA_Dx, StrokeTIA_history, StrokeTIA_Symptoms, STUDY_NUMBER, sympt, Sympt_latest, Sympt_worst,
sympt1, sympt2, sympt3, sympt4, Symptoms.3g, Symptoms.4g, Symptoms.5G, systolic, T_NUMBER, TARC, TARC_rank, TAT_plasma, TC_2016, TC_all, TC_avg, TC_clinic, TC_dif,
TC_final, TC_finalCU, TC_var, Testosterone, TG_2016, TG_all, TG_avg, TG_clinic, TG_dif, TG_final, TG_finalCU, TG_var, TGF, TGFB, TGFB_rank, thrombos, thrombus,
thrombus_location, thrombus_new, thrombus_organization, thrombus_organization_v2, thrombus_percentage, thyros2, thyrosta, Time_event_OR, TimeOR_latest,
TimeOR_latest_4g, TimeOR_worst, TimeOR_worst_4g, TIMP1, TIMP2, TISNOW, TNFA, TNFA_rank, totalchol, totalcholesterol_source, tractdig, tractdig2, tractdig3, tractdig4,
tractres, Treatment.DM, TREM1, triglyceride_source, triglyceriden, Tris_protein_conc_ug_ml_2015, Trop1, Trop1DT, Trop2, Trop2DT, Trop3, Trop3DT, TropmaxpostOK,
TropoMax, TropoMaxDT, tropomaxpositief, TSratio_blood, TSratio_plaque, UPID, validation_date, validation1, validation2, validation3, validation4, validation5,
validation6, VAR00001, VEGFA, VEGFA_plasma, VEGFA_rank, vegfa422, vessel_density, vessel_density_additional, vessel_density_averaged, vessel_density_Timo2012,
vessel_density_Timo2012_2, vessel_density_Timo2013, VesselDensity_LN, VesselDensity_rank, vitamin, vitamin2, vitb12, VRAGENLIJST, vWF_plasma, WBC_THAW,
Which.femoral.artery, Whichoperation, writtenIC, yearablo, yearablo2, yearablo3, yearace, yearace2, yearacet, yearanal, yearanal2, yearanal3, yearangi, yearanta,
yearanta2, yearanti, yearanti2, yearbblo, yearbblo2, yearcalc, yearcalc2, yearcalreg, yearcarb, yearchol, yearchol2, yearchol3, yearclau1, yearclau2, yearclop,
yearcom1, yearcom2, yearcom3, yearcort, yearcorthorm2, yearderm, yeardig, yeardig2, yeardig3, yeardig4, yeardipy, yeardiur, yeardiur2, yeardiur3, yearerec, yeareye,
yeargluc, yeargluc2, yeargluc3, yeargluc4, yeargrel, yearinsu, yeariron, yeariron2, yearneur, yearneur2, yearneur3, yearneur4, yearnitr, yearnitr2, yearOR_bin_2010,
YearOR_per2years, yearotant, yearotcor, yearoth2, yearothe, yearpros, yearpsy5, yearren, yearresp, yearrheu, yearrheu2, yearrheu3, yearsta2, yearstat, yeartemp,
yearthro, yearthyr, yearthyr2, yearvit2, yearvita, Yrs.no.smoking, Yrs.smoking
The following objects are masked from AEDB.CEA (pos = 9):
ABI_70, ABI_max, ABI_mean, ABI_min, ABI_OP, ablock, ablock2, ablock3, aceinhib, aceinhib2, acetylsa, Adiponectin_pg_ug_2015, AE_AAA_bijzonderheden, Age, Age_Q,
AgeGroup, AgeGroupSex, AgeSQR, aid, AlcoholUse, Aldosteron_recode, alg10201, alg10202, alg10203, alg10204, alg10205, alg105, alg106, alg109, alg110, alg113, alg114,
alg115, ALOX5, analg2, analg3, analgeti, Ang2, angioii, ANGPT2, anti_apoA1_IgG, anti_apoA1_index, anti_apoA1_na, antiall, antiall2, antiarrh, antiarrh2, ANXA2, AP_Dx,
AP_Dx1, AP_Dx2, APOB, artercon, Artery_summary, arteryop, AsymptSympt, AsymptSympt2G, bblock, bblock2, blocko, blocksnr, BMI, BMI_US, BMI_WHO, BMI30ormore, brain401,
brain402, brain403, brain404, brain405, brain406, brain407, brain408, brain409, brain410, brain411, brain412, brain413, brn40701, bspoed, CAD_Dx, CAD_Dx1, CAD_Dx2,
CAD_history, CADPAOD_history, Calc.bin, calcification, CalcificationPlaque, calcium, calcium2, calreg, carbasal, cardioembolic, Caspase3_7, CAV1, CD44, CD44V3,
CEA_or_CAS, CEL, CFD_recalc, cholverl, cholverl2, cholverl3, CI_history, clau1, clau2, Claudication, clopidog, CML, collagen, Collagen.bin, CollagenPlaque, combi1,
combi2, combi3, comorbidity.DM, concablo, concablo2, concablo3, concace2, concacei, concacet, concalle, concanal, concanal2, concanal3, concangi, concanta2, concanti,
concanti2, concbblo, concbblo2, conccalc, conccalc2, conccalreg, conccarb, concchol, concchol2, concchol3, concclau1, concclau2, concclop, conccom1, conccom2, conccom3,
conccort, conccorthorm2, concderm, concdig, concdig2, concdig3, concdig4, concdipy, concdiur, concdiur2, concdiur3, concerec, conceye, concgluc, concgluc2, concgluc3,
concgluc4, concgrel, concinsu, conciron, conciron2, concneur, concneur2, concneur3, concneur4, concnitr, concnitr2, concotant, concotcor, concoth2, concothe, concpros,
concpsy5, concren, concresp, concrheu, concrheu2, concrheu3, concsta2, concstat, concthro, concthyr, concthyr2, concvit2, concvita, Contralateral_surgery, conwhen,
corticos, cortihorm2, creat, crp_all, CRP_avg, CRP_dif, crp_source, CRP_var, CST3_pg_ug, CST3_serum_luminex, CTGF, cTNI_plasma, CTSA, CTSB, CTSL1, CTSS, cyr61,
date_ic_patient, date_ic_researcher, Date.of.birth, date.previous.operation, date1yr, date3mon, dateapprox_latest, dateapprox_worst, dateapprox1, dateapprox2,
dateapprox3, dateapprox4, dateend1, dateend2, dateend3, dateend4, dateend5, dateend6, dateexact_latest, dateexact_worst, dateexact1, dateexact2, dateexact3, dateexact4,
dateok, dermacor, DiabetesStatus, diastoli, diet801, diet802, diet803, diet804, diet805, diet806, diet807, diet808, diet809, diet810, diet811, diet812, diet813,
diet814, diet815, diet816, diet817, diet818, diet819, diet820, diet821, diet822, diet823, diet824, dipyridi, diuretic, diuretic2, diuretic3, DM, DM.composite,
duaalantiplatelet, duplend, eaindexl, eaindexr, eCigarettes, edaplaqu_recalc, edavrspl, EGR, EMMPRIN_45kD, EMMPRIN_58kD, ENDOGLIN, endpoint1, endpoint2, endpoint3,
endpoint4, endpoint5, endpoint6, Eotaxin1, Eotaxin1_rank, EP_CAD, ep_cad_t_30days, ep_cad_t_3years, EP_CAD_time, ep_cad.30days, EP_CI, ep_ci_t_30days, ep_ci_t_3years,
EP_CI_time, ep_com_t_30days, ep_com_t_3years, EP_composite, EP_composite_time, EP_coronary, ep_coronary_t_30days, ep_coronary_t_3years, ep_coronary_t_90days,
EP_coronary_time, EP_CVdeath, ep_cvdeath_t_30days, ep_cvdeath_t_3years, ep_cvdeath_t_90days, EP_CVdeath_time, EP_death, ep_death_t_30days, ep_death_t_3years,
EP_death_time, EP_fatalCVA, ep_fatalCVA_t_30days, ep_fatalCVA_t_3years, EP_fatalCVA_time, EP_hemorrhagic_stroke, ep_hemorrhagic_stroke_t_3years,
EP_hemorrhagic_stroke_time, ep_hemorrhagic_stroke.3years, EP_ischemic_stroke, ep_ischemic_stroke_t_3years, EP_ischemic_stroke_time, ep_ischemic_stroke.3years,
EP_leg_amputation, EP_leg_amputation_time, ep_legamputation_t_30days, ep_legamputation_t_3years, EP_major, ep_major_t_30days, ep_major_t_3years, ep_major_t_90days,
EP_major_time, EP_MI, ep_mi_t_30days, ep_mi_t_3years, EP_MI_time, EP_nonstroke_event, EP_nonstroke_event_time, ep_nonstroke_t_3years, EP_peripheral,
ep_peripheral_t_30days, ep_peripheral_t_3years, EP_peripheral_time, EP_pta, ep_pta_t_30days, ep_pta_t_3years, EP_pta_time, EP_stroke, ep_stroke_t_30days,
ep_stroke_t_3years, ep_stroke_t_90days, EP_stroke_time, EP_strokeCVdeath, ep_strokeCVdeath_t_30days, ep_strokeCVdeath_t_3years, EP_strokeCVdeath_time, EP_strokedeath,
ep_strokedeath_t_30days, ep_strokedeath_t_3years, EP_strokedeath_time, ePackYearsSmoking, epcad.3years, epci.30days, epci.3years, epcom.30days, epcom.3years,
epcoronary.30days, epcoronary.3years, epcoronary.90days, epcvdeath.30days, epcvdeath.3years, epcvdeath.90days, epdeath.30days, epdeath.3years, epfatalCVA.30days,
epfatalCVA.3years, eplegamputation.30days, eplegamputation.3years, epmajor.30days, epmajor.3years, epmajor.90days, epmi.30days, epmi.3years, epnonstroke.3years,
epperipheral.30days, epperipheral.3years, eppta.30days, eppta.3years, epstroke.30days, epstroke.3years, epstroke.90days, epstrokeCVdeath.30days, epstrokeCVdeath.3years,
epstrokedeath.30days, epstrokedeath.3years, erec, Estradiol, everstroke_composite, Everstroke_Ipsilateral, exer901, exer902, exer903, exer904, exer905, exer906,
exer9071, exer9072, exer9073, exer9074, exer9075, exer9076, exer908, exer909, exer910, eyedrop, EZis, FABP_serum, FABP4, FABP4_pg_ug, FABP4_serum_luminex, fat,
Fat.bin_10, Fat.bin_40, Fat10Perc, Femoral.interv, FH_AAA_broth, FH_AAA_comp, FH_AAA_mat, FH_AAA_parent, FH_AAA_pat, FH_AAA_sibling, FH_AAA_sis, FH_amp_broth,
FH_amp_comp, FH_amp_mat, FH_amp_parent, FH_amp_pat, FH_amp_sibling, FH_amp_sis, FH_CAD_broth, FH_CAD_comp, FH_CAD_mat, FH_CAD_parent, FH_CAD_pat, FH_CAD_sibling,
FH_CAD_sis, FH_corcalc_broth, FH_corcalc_comp, FH_corcalc_mat, FH_corcalc_parent, FH_corcalc_pat, FH_corcalc_sibling, FH_corcalc_sis, FH_CVD_broth, FH_CVD_comp,
FH_CVD_mat, FH_CVD_parent, FH_CVD_pat, FH_CVD_sibling, FH_CVD_sis, FH_CVdeath_broth, FH_CVdeath_comp, FH_CVdeath_mat, FH_CVdeath_parent, FH_CVdeath_pat,
FH_CVdeath_sibling, FH_CVdeath_sis, FH_DM_broth, FH_DM_comp, FH_DM_mat, FH_DM_parent, FH_DM_pat, FH_DM_sibling, FH_DM_sis, FH_HC_broth, FH_HC_comp, FH_HC_mat,
FH_HC_parent, FH_HC_pat, FH_HC_sibling, FH_HC_sis, FH_HT_broth, FH_HT_comp, FH_HT_mat, FH_HT_parent, FH_HT_pat, FH_HT_sibling, FH_HT_sis, FH_MI_broth, FH_MI_comp,
FH_MI_mat, FH_MI_parent, FH_MI_pat, FH_MI_sibling, FH_MI_sis, FH_otherCVD_broth, FH_otherCVD_comp, FH_otherCVD_mat, FH_otherCVD_parent, FH_otherCVD_pat,
FH_otherCVD_sibling, FH_otherCVD_sis, FH_PAD_broth, FH_PAD_comp, FH_PAD_mat, FH_PAD_parent, FH_PAD_pat, FH_PAD_sibling, FH_PAD_sis, FH_PAV_broth, FH_PAV_comp,
FH_PAV_mat, FH_PAV_parent, FH_PAV_pat, FH_PAV_sibling, FH_PAV_sis, FH_POB_broth, FH_POB_comp, FH_POB_mat, FH_POB_parent, FH_POB_pat, FH_POB_sibling, FH_POB_sis,
FH_risk_broth, FH_risk_comp, FH_risk_mat, FH_risk_parent, FH_risk_pat, FH_risk_sibling, FH_risk_sis, FH_Stroke_broth, FH_Stroke_comp, FH_Stroke_mat, FH_Stroke_parent,
FH_Stroke_pat, FH_Stroke_sibling, FH_Stroke_sis, FH_tromb_broth, FH_tromb_comp, FH_tromb_mat, FH_tromb_parent, FH_tromb_pat, FH_tromb_sibling, FH_tromb_sis, filter_$,
folicaci, followup1, followup2, followup3, Fontaine, FU_check, FU_check_date, FU.cutt.off.30days, FU.cutt.off.3years, FU.cutt.off.90days, FU1JAAR, FU2JAAR, FU3JAAR,
FURIN_low, FURIN_up, GDF15_plasma, geen_med, Gender, GFR_CG, GFR_MDRD, glucose, GR_Segment, GrB_plaque, GrB_serum, grel, GrK_plaque, GrK_serum, GrM_plaque, GrM_serum,
HA, hb, HDAC9, HDL, HDL_2016, HDL_all, HDL_avg, HDL_clinic, HDL_dif, HDL_final, HDL_finalCU, hdl_source, HDL_var, heart300, heart301, heart302, heart303, heart304,
heart305, heart306, heart307, heart308, heart309, heart310, heart311, heart312, heart313, heart314, heart315, heart316, heart317, heart318, heart319, heart320,
heart321, heart322, heart323, heart324, heart325, heart326, heart327, heart328, HIF1A, ho1, homocys, Hospital, hrt31301, hsCRP_plasma, ht, HYAL55KD, HYALURON,
Hypertension.composite, Hypertension.drugs, Hypertension.selfreport, Hypertension.selfreportdrug, Hypertension1, Hypertension2, IL1_Beta, IL10, IL10_rank, IL12,
IL12_rank, IL13, IL13_rank, IL17, IL2, IL2_rank, IL21, IL21_rank, IL4, IL4_rank, IL5, IL5_rank, IL6, IL6_pg_ug_2015, IL6_rank, IL6R_pg_ug_2015, IL8, IL8_pg_ug_2015,
IL8_rank, IL9, IL9_rank, indexsymptoms_latest, indexsymptoms_latest_4g, indexsymptoms_worst, indexsymptoms_worst_4g, INFG, INFG_rank, informedconsent, insulin,
insuline, INVULDAT, IP10, IP10_rank, IPH, IPH_extended.bin, IPH.bin, ironfoli, ironfoli2, KDOQI, latest, LDL, LDL_2016, LDL_all, LDL_avg, LDL_clinic, LDL_dif,
LDL_final, LDL_finalCU, ldl_source, LDL_var, leg501, leg502, leg503, leg504, leg505, leg506, leg507, leg508, leg509, leg510, leg511, leg512, leg513, leg514, leg515,
leg516, leg517, leg518, leg519, leg520, LMW1STME, LTB4, LTB4R, macmean0, macrophages, Macrophages_LN, macrophages_location, Macrophages_rank, Macrophages.bin, MAP,
Mast_cells_plaque, max.followup, MCP1, MCP1_pg_ug_2015, MCP1_pg_ug_2015_LN, MCP1_pg_ug_2015_rank, MCP1_rank, MCSF_pg_ug_2015, MDC, MDC_rank, Med_notes, Med.ablock,
Med.ACE_inh, Med.acetylsal, Med.acetylsal_Combi1, Med.acetylsal_Combi2, Med.acetylsal_Combi3, Med.ADPinh, Med.all.antiplatelet, Med.angiot2.antag, Med.antiarrh,
Med.anticoagulants, Med.ascal, Med.aspirin.derived, Med.bblocker, Med.calc_antag, Med.dipyridamole, Med.diuretic, Med.LLD, Med.nitrate, Med.otheranthyp, Med.renin,
Med.statin, Med.statin.derived, Med.Statin.LLD, Med.statin2, MedHx_CVD, media, MG_H1, MI_Dx, MI_Dx1, MI_Dx2, MIF, MIF_rank, MIG, MIG_rank, MIP1a, MIP1a_rank,
miRNA100_RNU19, miRNA100_RNU48, miRNA155_RNU19, miRNA155_RNU48, MMP14, MMP2, MMP2_rank, MMP2TIMP2, MMP8, MMP8_rank, MMP9, MMP9_rank, MMP9TIMP1, MPO_plasma, MRP_14,
MRP_8, MRP_8_14C, MRP_8_14C_buhlmann, MRP14_plasma, MRP8_14C_plasma, MRP8_plasma, negatibl, neuropsy, neuropsy2, neuropsy3, neuropsy4, neurpsy5, neutrophils, NGAL,
NGAL_low, NGAL_MMP9_complex, NGAL_MMP9_local, NGAL_MMP9_peripheral, NGAL_total, NGAL_up, nitrate, nitrate2, NOD1, NOD2, nogobt1_recalculated, NTproBNP_plasma,
Number_Events_Sorter, Number_Sorted_CD14, Number_Sorted_CD20, Number_Sorted_CD4_Cells, Number_Sorted_CD8_Cells, oac701, oac702, oac70305, oac704, oac705, oac706,
oac707, oac708, oac709, oac710, oac711, oac712, oac713, oac714, OKyear, OPG, OPG_plasma, OPG_rank, OPN, OPN_2013, OPN_plasma, OR_blood, Oral.glucose.inh, oralgluc,
oralgluc2, oralgluc3, oralgluc4, ORyear, othanthyp, othcoron, other, other2, OverallPlaquePhenotype, PAI1_pg_ug_2015, PAOD, PARC, PARC_rank, patch, PCSK9_plasma,
PDGF_BB_plasma, Percentage_CD14, Percentage_CD20, Percentage_CD4, Percentage_CD8, Peripheral.interv, PKC, PLA2_plasma, plaquephenotype, positibl, PrimaryLast,
PrimaryLast1, prostagl, PulsePressure, qual01, qual02, qual0301, qual0302, qual0303, qual0304, qual0305, qual0306, qual0307, qual0308, qual0309, qual0310, qual0401,
qual0402, qual0403, qual0404, qual0501, qual0502, qual0503, qual06, qual07, qual08, qual0901, qual0902, qual0903, qual0904, qual0905, qual0906, qual0907, qual0908,
qual0909, qual1010, qual1101, qual1102, qual1103, qual1104, RAAS_med, RANTES, RANTES_pg_ug_2015, RANTES_plasma, RANTES_rank, Ras, RE50_01, RE70_01, Renine_recode,
renineinh, restenos, restenosisOK, rheuma, rheuma2, rheuma3, risk601, risk602, risk603, risk604, risk605, risk606, risk607, risk608, risk609, risk610, risk611, risk612,
risk613, risk614, risk615, risk616, risk617, risk618, risk619, risk620, SHBG, sICAM1, sICAM1_rank, SMAD1_5_8, SMAD2, SMAD3, smc, SMC_LN, smc_location,
smc_macrophages_ratio, SMC_rank, SMC.bin, smcmean0, SmokerCurrent, SmokerStatus, SmokingReported, SmokingYearOR, stat3P, statin2, statines, ste3mext, sten1yr, sten3mo,
stenose, stenosis_con_bin, Stenosis_contralateral, Stenosis_ipsilateral, Stroke_Dx, Stroke_eitherside, Stroke_history, Stroke_Symptoms, StrokeTIA_Dx, StrokeTIA_history,
StrokeTIA_Symptoms, STUDY_NUMBER, sympt, Sympt_latest, Sympt_worst, sympt1, sympt2, sympt3, sympt4, Symptoms.3g, Symptoms.4g, Symptoms.5G, systolic, T_NUMBER, TARC,
TARC_rank, TAT_plasma, TC_2016, TC_all, TC_avg, TC_clinic, TC_dif, TC_final, TC_finalCU, TC_var, Testosterone, TG_2016, TG_all, TG_avg, TG_clinic, TG_dif, TG_final,
TG_finalCU, TG_var, TGF, TGFB, TGFB_rank, thrombos, thrombus, thrombus_location, thrombus_new, thrombus_organization, thrombus_organization_v2, thrombus_percentage,
thyros2, thyrosta, Time_event_OR, TimeOR_latest, TimeOR_latest_4g, TimeOR_worst, TimeOR_worst_4g, TIMP1, TIMP2, TISNOW, TNFA, TNFA_rank, totalchol,
totalcholesterol_source, tractdig, tractdig2, tractdig3, tractdig4, tractres, Treatment.DM, TREM1, triglyceride_source, triglyceriden, Trop1, Trop1DT, Trop2, Trop2DT,
Trop3, Trop3DT, TropmaxpostOK, TropoMax, TropoMaxDT, tropomaxpositief, TSratio_blood, TSratio_plaque, UPID, validation_date, validation1, validation2, validation3,
validation4, validation5, validation6, VAR00001, VEGFA, VEGFA_plasma, VEGFA_rank, vegfa422, vessel_density, vessel_density_additional, vessel_density_averaged,
vessel_density_Timo2012, vessel_density_Timo2012_2, vessel_density_Timo2013, VesselDensity_LN, VesselDensity_rank, vitamin, vitamin2, vitb12, VRAGENLIJST, vWF_plasma,
WBC_THAW, Which.femoral.artery, Whichoperation, writtenIC, yearablo, yearablo2, yearablo3, yearace, yearace2, yearacet, yearanal, yearanal2, yearanal3, yearangi,
yearanta, yearanta2, yearanti, yearanti2, yearbblo, yearbblo2, yearcalc, yearcalc2, yearcalreg, yearcarb, yearchol, yearchol2, yearchol3, yearclau1, yearclau2,
yearclop, yearcom1, yearcom2, yearcom3, yearcort, yearcorthorm2, yearderm, yeardig, yeardig2, yeardig3, yeardig4, yeardipy, yeardiur, yeardiur2, yeardiur3, yearerec,
yeareye, yeargluc, yeargluc2, yeargluc3, yeargluc4, yeargrel, yearinsu, yeariron, yeariron2, yearneur, yearneur2, yearneur3, yearneur4, yearnitr, yearnitr2,
yearOR_bin_2010, YearOR_per2years, yearotant, yearotcor, yearoth2, yearothe, yearpros, yearpsy5, yearren, yearresp, yearrheu, yearrheu2, yearrheu3, yearsta2, yearstat,
yearthro, yearthyr, yearthyr2, yearvit2, yearvita, Yrs.no.smoking, Yrs.smoking# mac instability
AEDB.CEA[,"MAC_Instability"] <- NA
AEDB.CEA$MAC_Instability[Macrophages.bin == -999] <- NA
AEDB.CEA$MAC_Instability[Macrophages.bin == "no/minor"] <- 0
AEDB.CEA$MAC_Instability[Macrophages.bin == "moderate/heavy"] <- 1
# fat instability
AEDB.CEA[,"FAT10_Instability"] <- NA
AEDB.CEA$FAT10_Instability[Fat.bin_10 == -999] <- NA
AEDB.CEA$FAT10_Instability[Fat.bin_10 == " <10%"] <- 0
AEDB.CEA$FAT10_Instability[Fat.bin_10 == " >10%"] <- 1
# col instability
AEDB.CEA[,"COL_Instability"] <- NA
AEDB.CEA$COL_Instability[Collagen.bin == -999] <- NA
AEDB.CEA$COL_Instability[Collagen.bin == "no/minor"] <- 1
AEDB.CEA$COL_Instability[Collagen.bin == "moderate/heavy"] <- 0
# smc instability
AEDB.CEA[,"SMC_Instability"] <- NA
AEDB.CEA$SMC_Instability[SMC.bin == -999] <- NA
AEDB.CEA$SMC_Instability[SMC.bin == "no/minor"] <- 1
AEDB.CEA$SMC_Instability[SMC.bin == "moderate/heavy"] <- 0
# iph instability
AEDB.CEA[,"IPH_Instability"] <- NA
AEDB.CEA$IPH_Instability[IPH.bin == -999] <- NA
AEDB.CEA$IPH_Instability[IPH.bin == "no"] <- 0
AEDB.CEA$IPH_Instability[IPH.bin == "yes"] <- 1
detach(AEDB.CEA)
table(AEDB.CEA$MAC_Instability, useNA = "ifany")
0 1 <NA>
847 992 584 table(AEDB.CEA$FAT10_Instability, useNA = "ifany")
0 1 <NA>
542 1316 565 table(AEDB.CEA$COL_Instability, useNA = "ifany")
0 1 <NA>
1469 382 572 table(AEDB.CEA$SMC_Instability, useNA = "ifany")
0 1 <NA>
1244 602 577 table(AEDB.CEA$IPH_Instability, useNA = "ifany")
0 1 <NA>
746 1108 569 # creating vulnerability index
AEDB.CEA <- AEDB.CEA %>% mutate(Plaque_Vulnerability_Index = factor(rowSums(.[grep("_Instability", names(.))], na.rm = TRUE)),
)
table(AEDB.CEA$Plaque_Vulnerability_Index, useNA = "ifany")
0 1 2 3 4 5
713 348 479 535 251 97 # str(AEDB.CEA$Plaque_Vulnerability_Index)
Here we plot the levels of inverse-rank normal transformed MCP1 plaque levels from experiment 1 and 2 to the Plaque vulnerability index.
library(sjlabelled)
attach(AEDB.CEA)The following objects are masked from AEDB.CEA (pos = 3):
ABI_70, ABI_max, ABI_mean, ABI_min, ABI_OP, ablock, ablock2, ablock3, aceinhib, aceinhib2, acetylsa, Adiponectin_ng_ml_2015, Adiponectin_pg_ug_2015,
AE_AAA_bijzonderheden, Age, Age_Q, AgeGroup, AgeGroupSex, AgeSQR, aid, AlcoholUse, Aldosteron_recode, alg10201, alg10202, alg10203, alg10204, alg10205, alg105, alg106,
alg109, alg110, alg113, alg114, alg115, ALOX5, analg2, analg3, analgeti, Ang2, angioii, ANGPT2, anti_apoA1_IgG, anti_apoA1_index, anti_apoA1_na, antiall, antiall2,
antiarrh, antiarrh2, ANXA2, AP_Dx, AP_Dx1, AP_Dx2, APOB, artercon, Artery_summary, arteryop, AsymptSympt, AsymptSympt2G, bblock, bblock2, blocko, blocksnr, BMI, BMI_US,
BMI_WHO, BMI30ormore, brain401, brain402, brain403, brain404, brain405, brain406, brain407, brain408, brain409, brain410, brain411, brain412, brain413, brn40701,
bspoed, CAD_Dx, CAD_Dx1, CAD_Dx2, CAD_history, CADPAOD_history, Calc.bin, calcification, CalcificationPlaque, calcium, calcium2, calreg, carbasal, cardioembolic,
Caspase3_7, CAV1, CD44, CD44V3, CEA_or_CAS, CEL, CFD_recalc, cholverl, cholverl2, cholverl3, CI_history, clau1, clau2, Claudication, clopidog, CML, COL_Instability,
collagen, Collagen.bin, CollagenPlaque, combi1, combi2, combi3, comorbidity.DM, concablo, concablo2, concablo3, concace2, concacei, concacet, concalle, concanal,
concanal2, concanal3, concangi, concanta2, concanti, concanti2, concbblo, concbblo2, conccalc, conccalc2, conccalreg, conccarb, concchol, concchol2, concchol3,
concclau1, concclau2, concclop, conccom1, conccom2, conccom3, conccort, conccorthorm2, concderm, concdig, concdig2, concdig3, concdig4, concdipy, concdiur, concdiur2,
concdiur3, concerec, conceye, concgluc, concgluc2, concgluc3, concgluc4, concgrel, concinsu, conciron, conciron2, concneur, concneur2, concneur3, concneur4, concnitr,
concnitr2, concotant, concotcor, concoth2, concothe, concpros, concpsy5, concren, concresp, concrheu, concrheu2, concrheu3, concsta2, concstat, concthro, concthyr,
concthyr2, concvit2, concvita, Contralateral_surgery, conwhen, corticos, cortihorm2, creat, crp_all, CRP_avg, CRP_dif, crp_source, CRP_var, CST3_pg_ug,
CST3_serum_luminex, CTGF, cTNI_plasma, CTSA, CTSB, CTSL1, CTSS, cyr61, date_ic_patient, date_ic_researcher, Date.of.birth, date.previous.operation, date1yr, date3mon,
dateapprox_latest, dateapprox_worst, dateapprox1, dateapprox2, dateapprox3, dateapprox4, dateend1, dateend2, dateend3, dateend4, dateend5, dateend6, dateexact_latest,
dateexact_worst, dateexact1, dateexact2, dateexact3, dateexact4, dateok, dermacor, DiabetesStatus, diastoli, diet801, diet802, diet803, diet804, diet805, diet806,
diet807, diet808, diet809, diet810, diet811, diet812, diet813, diet814, diet815, diet816, diet817, diet818, diet819, diet820, diet821, diet822, diet823, diet824,
dipyridi, diuretic, diuretic2, diuretic3, DM, DM.composite, duaalantiplatelet, duplend, eaindexl, eaindexr, eCigarettes, edaplaqu_recalc, edavrspl, EGR, EMMPRIN_45kD,
EMMPRIN_58kD, ENDOGLIN, endpoint1, endpoint2, endpoint3, endpoint4, endpoint5, endpoint6, Eotaxin1, Eotaxin1_rank, EP_CAD, ep_cad_t_30days, ep_cad_t_3years,
EP_CAD_time, ep_cad.30days, EP_CI, ep_ci_t_30days, ep_ci_t_3years, EP_CI_time, ep_com_t_30days, ep_com_t_3years, EP_composite, EP_composite_time, EP_coronary,
ep_coronary_t_30days, ep_coronary_t_3years, ep_coronary_t_90days, EP_coronary_time, EP_CVdeath, ep_cvdeath_t_30days, ep_cvdeath_t_3years, ep_cvdeath_t_90days,
EP_CVdeath_time, EP_death, ep_death_t_30days, ep_death_t_3years, EP_death_time, EP_fatalCVA, ep_fatalCVA_t_30days, ep_fatalCVA_t_3years, EP_fatalCVA_time,
EP_hemorrhagic_stroke, ep_hemorrhagic_stroke_t_3years, EP_hemorrhagic_stroke_time, ep_hemorrhagic_stroke.3years, EP_ischemic_stroke, ep_ischemic_stroke_t_3years,
EP_ischemic_stroke_time, ep_ischemic_stroke.3years, EP_leg_amputation, EP_leg_amputation_time, ep_legamputation_t_30days, ep_legamputation_t_3years, EP_major,
ep_major_t_30days, ep_major_t_3years, ep_major_t_90days, EP_major_time, EP_MI, ep_mi_t_30days, ep_mi_t_3years, EP_MI_time, EP_nonstroke_event, EP_nonstroke_event_time,
ep_nonstroke_t_3years, EP_peripheral, ep_peripheral_t_30days, ep_peripheral_t_3years, EP_peripheral_time, EP_pta, ep_pta_t_30days, ep_pta_t_3years, EP_pta_time,
EP_stroke, ep_stroke_t_30days, ep_stroke_t_3years, ep_stroke_t_90days, EP_stroke_time, EP_strokeCVdeath, ep_strokeCVdeath_t_30days, ep_strokeCVdeath_t_3years,
EP_strokeCVdeath_time, EP_strokedeath, ep_strokedeath_t_30days, ep_strokedeath_t_3years, EP_strokedeath_time, ePackYearsSmoking, epcad.3years, epci.30days, epci.3years,
epcom.30days, epcom.3years, epcoronary.30days, epcoronary.3years, epcoronary.90days, epcvdeath.30days, epcvdeath.3years, epcvdeath.90days, epdeath.30days,
epdeath.3years, epfatalCVA.30days, epfatalCVA.3years, eplegamputation.30days, eplegamputation.3years, epmajor.30days, epmajor.3years, epmajor.90days, epmi.30days,
epmi.3years, epnonstroke.3years, epperipheral.30days, epperipheral.3years, eppta.30days, eppta.3years, epstroke.30days, epstroke.3years, epstroke.90days,
epstrokeCVdeath.30days, epstrokeCVdeath.3years, epstrokedeath.30days, epstrokedeath.3years, erec, Estradiol, everstroke_composite, Everstroke_Ipsilateral, exer901,
exer902, exer903, exer904, exer905, exer906, exer9071, exer9072, exer9073, exer9074, exer9075, exer9076, exer908, exer909, exer910, eyedrop, EZis, FABP_serum, FABP4,
FABP4_pg_ug, FABP4_serum_luminex, fat, Fat.bin_10, Fat.bin_40, FAT10_Instability, Fat10Perc, Femoral.interv, FH_AAA_broth, FH_AAA_comp, FH_AAA_mat, FH_AAA_parent,
FH_AAA_pat, FH_AAA_sibling, FH_AAA_sis, FH_amp_broth, FH_amp_comp, FH_amp_mat, FH_amp_parent, FH_amp_pat, FH_amp_sibling, FH_amp_sis, FH_CAD_broth, FH_CAD_comp,
FH_CAD_mat, FH_CAD_parent, FH_CAD_pat, FH_CAD_sibling, FH_CAD_sis, FH_corcalc_broth, FH_corcalc_comp, FH_corcalc_mat, FH_corcalc_parent, FH_corcalc_pat,
FH_corcalc_sibling, FH_corcalc_sis, FH_CVD_broth, FH_CVD_comp, FH_CVD_mat, FH_CVD_parent, FH_CVD_pat, FH_CVD_sibling, FH_CVD_sis, FH_CVdeath_broth, FH_CVdeath_comp,
FH_CVdeath_mat, FH_CVdeath_parent, FH_CVdeath_pat, FH_CVdeath_sibling, FH_CVdeath_sis, FH_DM_broth, FH_DM_comp, FH_DM_mat, FH_DM_parent, FH_DM_pat, FH_DM_sibling,
FH_DM_sis, FH_HC_broth, FH_HC_comp, FH_HC_mat, FH_HC_parent, FH_HC_pat, FH_HC_sibling, FH_HC_sis, FH_HT_broth, FH_HT_comp, FH_HT_mat, FH_HT_parent, FH_HT_pat,
FH_HT_sibling, FH_HT_sis, FH_MI_broth, FH_MI_comp, FH_MI_mat, FH_MI_parent, FH_MI_pat, FH_MI_sibling, FH_MI_sis, FH_otherCVD_broth, FH_otherCVD_comp, FH_otherCVD_mat,
FH_otherCVD_parent, FH_otherCVD_pat, FH_otherCVD_sibling, FH_otherCVD_sis, FH_PAD_broth, FH_PAD_comp, FH_PAD_mat, FH_PAD_parent, FH_PAD_pat, FH_PAD_sibling, FH_PAD_sis,
FH_PAV_broth, FH_PAV_comp, FH_PAV_mat, FH_PAV_parent, FH_PAV_pat, FH_PAV_sibling, FH_PAV_sis, FH_POB_broth, FH_POB_comp, FH_POB_mat, FH_POB_parent, FH_POB_pat,
FH_POB_sibling, FH_POB_sis, FH_risk_broth, FH_risk_comp, FH_risk_mat, FH_risk_parent, FH_risk_pat, FH_risk_sibling, FH_risk_sis, FH_Stroke_broth, FH_Stroke_comp,
FH_Stroke_mat, FH_Stroke_parent, FH_Stroke_pat, FH_Stroke_sibling, FH_Stroke_sis, FH_tromb_broth, FH_tromb_comp, FH_tromb_mat, FH_tromb_parent, FH_tromb_pat,
FH_tromb_sibling, FH_tromb_sis, filter_$, folicaci, followup1, followup2, followup3, Fontaine, FU_check, FU_check_date, FU.cutt.off.30days, FU.cutt.off.3years,
FU.cutt.off.90days, FU1JAAR, FU2JAAR, FU3JAAR, FURIN_low, FURIN_up, GDF15_plasma, geen_med, Gender, GFR_CG, GFR_MDRD, glucose, GR_Segment, GrB_plaque, GrB_serum, grel,
GrK_plaque, GrK_serum, GrM_plaque, GrM_serum, HA, hb, HDAC9, HDL, HDL_2016, HDL_all, HDL_avg, HDL_clinic, HDL_dif, HDL_final, HDL_finalCU, hdl_source, HDL_var,
heart300, heart301, heart302, heart303, heart304, heart305, heart306, heart307, heart308, heart309, heart310, heart311, heart312, heart313, heart314, heart315,
heart316, heart317, heart318, heart319, heart320, heart321, heart322, heart323, heart324, heart325, heart326, heart327, heart328, HIF1A, ho1, homocys, Hospital,
hrt31301, hsCRP_plasma, ht, HYAL55KD, HYALURON, Hypertension.composite, Hypertension.drugs, Hypertension.selfreport, Hypertension.selfreportdrug, Hypertension1,
Hypertension2, IL1_Beta, IL10, IL10_rank, IL12, IL12_rank, IL13, IL13_rank, IL17, IL2, IL2_rank, IL21, IL21_rank, IL4, IL4_rank, IL5, IL5_rank, IL6, IL6_pg_ml_2015,
IL6_pg_ug_2015, IL6_rank, IL6R_pg_ml_2015, IL6R_pg_ug_2015, IL8, IL8_pg_ml_2015, IL8_pg_ug_2015, IL8_rank, IL9, IL9_rank, indexsymptoms_latest, indexsymptoms_latest_4g,
indexsymptoms_worst, indexsymptoms_worst_4g, INFG, INFG_rank, informedconsent, insulin, insuline, INVULDAT, IP10, IP10_rank, IPH, IPH_extended.bin, IPH_Instability,
IPH.bin, ironfoli, ironfoli2, KDOQI, latest, LDL, LDL_2016, LDL_all, LDL_avg, LDL_clinic, LDL_dif, LDL_final, LDL_finalCU, ldl_source, LDL_var, leg501, leg502, leg503,
leg504, leg505, leg506, leg507, leg508, leg509, leg510, leg511, leg512, leg513, leg514, leg515, leg516, leg517, leg518, leg519, leg520, LMW1STME, LTB4, LTB4R,
MAC_binned, MAC_grouped, MAC_Instability, macmean0, macrophages, Macrophages_LN, macrophages_location, Macrophages_rank, Macrophages.bin, MAP, Mast_cells_plaque,
max.followup, MCP1, MCP1_LN, MCP1_pg_ml_2015, MCP1_pg_ml_2015_LN, MCP1_pg_ml_2015_rank, MCP1_pg_ug_2015, MCP1_pg_ug_2015_LN, MCP1_pg_ug_2015_rank, MCP1_rank,
MCSF_pg_ml_2015, MCSF_pg_ug_2015, MDC, MDC_rank, Med_notes, Med.ablock, Med.ACE_inh, Med.acetylsal, Med.acetylsal_Combi1, Med.acetylsal_Combi2, Med.acetylsal_Combi3,
Med.ADPinh, Med.all.antiplatelet, Med.angiot2.antag, Med.antiarrh, Med.anticoagulants, Med.ascal, Med.aspirin.derived, Med.bblocker, Med.calc_antag, Med.dipyridamole,
Med.diuretic, Med.LLD, Med.nitrate, Med.otheranthyp, Med.renin, Med.statin, Med.statin.derived, Med.Statin.LLD, Med.statin2, MedHx_CVD, media, MG_H1, MI_Dx, MI_Dx1,
MI_Dx2, MIF, MIF_rank, MIG, MIG_rank, MIP1a, MIP1a_rank, miRNA100_RNU19, miRNA100_RNU48, miRNA155_RNU19, miRNA155_RNU48, MMP14, MMP2, MMP2_rank, MMP2TIMP2, MMP8,
MMP8_rank, MMP9, MMP9_rank, MMP9TIMP1, MPO_plasma, MRP_14, MRP_8, MRP_8_14C, MRP_8_14C_buhlmann, MRP14_plasma, MRP8_14C_plasma, MRP8_plasma, negatibl, neuropsy,
neuropsy2, neuropsy3, neuropsy4, neurpsy5, neutrophils, NGAL, NGAL_low, NGAL_MMP9_complex, NGAL_MMP9_local, NGAL_MMP9_peripheral, NGAL_total, NGAL_up, nitrate,
nitrate2, NOD1, NOD2, nogobt1_recalculated, NTproBNP_plasma, Number_Events_Sorter, Number_Sorted_CD14, Number_Sorted_CD20, Number_Sorted_CD4_Cells,
Number_Sorted_CD8_Cells, oac701, oac702, oac70305, oac704, oac705, oac706, oac707, oac708, oac709, oac710, oac711, oac712, oac713, oac714, OKyear, OPG, OPG_plasma,
OPG_rank, OPN, OPN_2013, OPN_plasma, OR_blood, Oral.glucose.inh, oralgluc, oralgluc2, oralgluc3, oralgluc4, ORdate_epoch, ORdate_year, ORyear, ORyearGroup, othanthyp,
othcoron, other, other2, OverallPlaquePhenotype, PAI1_pg_ml_2015, PAI1_pg_ug_2015, PAOD, PARC, PARC_rank, patch, PCSK9_plasma, PDGF_BB_plasma, Percentage_CD14,
Percentage_CD20, Percentage_CD4, Percentage_CD8, Peripheral.interv, PKC, PLA2_plasma, Plaque_Vulnerability_Index, plaquephenotype, positibl, PrimaryLast, PrimaryLast1,
prostagl, PulsePressure, qual01, qual02, qual0301, qual0302, qual0303, qual0304, qual0305, qual0306, qual0307, qual0308, qual0309, qual0310, qual0401, qual0402,
qual0403, qual0404, qual0501, qual0502, qual0503, qual06, qual07, qual08, qual0901, qual0902, qual0903, qual0904, qual0905, qual0906, qual0907, qual0908, qual0909,
qual1010, qual1101, qual1102, qual1103, qual1104, RAAS_med, RANTES, RANTES_pg_ml_2015, RANTES_pg_ug_2015, RANTES_plasma, RANTES_rank, Ras, RE50_01, RE70_01,
Renine_recode, renineinh, restenos, restenosisOK, rheuma, rheuma2, rheuma3, risk601, risk602, risk603, risk604, risk605, risk606, risk607, risk608, risk609, risk610,
risk611, risk612, risk613, risk614, risk615, risk616, risk617, risk618, risk619, risk620, Segment_isolated_Tris_2015, SHBG, sICAM1, sICAM1_rank, SMAD1_5_8, SMAD2,
SMAD3, smc, SMC_binned, SMC_grouped, SMC_Instability, SMC_LN, smc_location, smc_macrophages_ratio, SMC_rank, SMC.bin, smcmean0, SmokerCurrent, SmokerStatus,
SmokingReported, SmokingYearOR, stat3P, statin2, statines, ste3mext, sten1yr, sten3mo, stenose, stenosis_con_bin, Stenosis_contralateral, Stenosis_ipsilateral,
Stroke_Dx, Stroke_eitherside, Stroke_history, Stroke_Symptoms, StrokeTIA_Dx, StrokeTIA_history, StrokeTIA_Symptoms, STUDY_NUMBER, sympt, Sympt_latest, Sympt_worst,
sympt1, sympt2, sympt3, sympt4, Symptoms.3g, Symptoms.4g, Symptoms.5G, systolic, T_NUMBER, TARC, TARC_rank, TAT_plasma, TC_2016, TC_all, TC_avg, TC_clinic, TC_dif,
TC_final, TC_finalCU, TC_var, Testosterone, TG_2016, TG_all, TG_avg, TG_clinic, TG_dif, TG_final, TG_finalCU, TG_var, TGF, TGFB, TGFB_rank, thrombos, thrombus,
thrombus_location, thrombus_new, thrombus_organization, thrombus_organization_v2, thrombus_percentage, thyros2, thyrosta, Time_event_OR, TimeOR_latest,
TimeOR_latest_4g, TimeOR_worst, TimeOR_worst_4g, TIMP1, TIMP2, TISNOW, TNFA, TNFA_rank, totalchol, totalcholesterol_source, tractdig, tractdig2, tractdig3, tractdig4,
tractres, Treatment.DM, TREM1, triglyceride_source, triglyceriden, Tris_protein_conc_ug_ml_2015, Trop1, Trop1DT, Trop2, Trop2DT, Trop3, Trop3DT, TropmaxpostOK,
TropoMax, TropoMaxDT, tropomaxpositief, TSratio_blood, TSratio_plaque, UPID, validation_date, validation1, validation2, validation3, validation4, validation5,
validation6, VAR00001, VEGFA, VEGFA_plasma, VEGFA_rank, vegfa422, vessel_density, vessel_density_additional, vessel_density_averaged, vessel_density_Timo2012,
vessel_density_Timo2012_2, vessel_density_Timo2013, VesselDensity_LN, VesselDensity_rank, vitamin, vitamin2, vitb12, VRAGENLIJST, vWF_plasma, WBC_THAW,
Which.femoral.artery, Whichoperation, writtenIC, yearablo, yearablo2, yearablo3, yearace, yearace2, yearacet, yearanal, yearanal2, yearanal3, yearangi, yearanta,
yearanta2, yearanti, yearanti2, yearbblo, yearbblo2, yearcalc, yearcalc2, yearcalreg, yearcarb, yearchol, yearchol2, yearchol3, yearclau1, yearclau2, yearclop,
yearcom1, yearcom2, yearcom3, yearcort, yearcorthorm2, yearderm, yeardig, yeardig2, yeardig3, yeardig4, yeardipy, yeardiur, yeardiur2, yeardiur3, yearerec, yeareye,
yeargluc, yeargluc2, yeargluc3, yeargluc4, yeargrel, yearinsu, yeariron, yeariron2, yearneur, yearneur2, yearneur3, yearneur4, yearnitr, yearnitr2, yearOR_bin_2010,
YearOR_per2years, yearotant, yearotcor, yearoth2, yearothe, yearpros, yearpsy5, yearren, yearresp, yearrheu, yearrheu2, yearrheu3, yearsta2, yearstat, yeartemp,
yearthro, yearthyr, yearthyr2, yearvit2, yearvita, Yrs.no.smoking, Yrs.smoking
The following objects are masked from AEDB.CEA (pos = 9):
ABI_70, ABI_max, ABI_mean, ABI_min, ABI_OP, ablock, ablock2, ablock3, aceinhib, aceinhib2, acetylsa, Adiponectin_pg_ug_2015, AE_AAA_bijzonderheden, Age, Age_Q,
AgeGroup, AgeGroupSex, AgeSQR, aid, AlcoholUse, Aldosteron_recode, alg10201, alg10202, alg10203, alg10204, alg10205, alg105, alg106, alg109, alg110, alg113, alg114,
alg115, ALOX5, analg2, analg3, analgeti, Ang2, angioii, ANGPT2, anti_apoA1_IgG, anti_apoA1_index, anti_apoA1_na, antiall, antiall2, antiarrh, antiarrh2, ANXA2, AP_Dx,
AP_Dx1, AP_Dx2, APOB, artercon, Artery_summary, arteryop, AsymptSympt, AsymptSympt2G, bblock, bblock2, blocko, blocksnr, BMI, BMI_US, BMI_WHO, BMI30ormore, brain401,
brain402, brain403, brain404, brain405, brain406, brain407, brain408, brain409, brain410, brain411, brain412, brain413, brn40701, bspoed, CAD_Dx, CAD_Dx1, CAD_Dx2,
CAD_history, CADPAOD_history, Calc.bin, calcification, CalcificationPlaque, calcium, calcium2, calreg, carbasal, cardioembolic, Caspase3_7, CAV1, CD44, CD44V3,
CEA_or_CAS, CEL, CFD_recalc, cholverl, cholverl2, cholverl3, CI_history, clau1, clau2, Claudication, clopidog, CML, collagen, Collagen.bin, CollagenPlaque, combi1,
combi2, combi3, comorbidity.DM, concablo, concablo2, concablo3, concace2, concacei, concacet, concalle, concanal, concanal2, concanal3, concangi, concanta2, concanti,
concanti2, concbblo, concbblo2, conccalc, conccalc2, conccalreg, conccarb, concchol, concchol2, concchol3, concclau1, concclau2, concclop, conccom1, conccom2, conccom3,
conccort, conccorthorm2, concderm, concdig, concdig2, concdig3, concdig4, concdipy, concdiur, concdiur2, concdiur3, concerec, conceye, concgluc, concgluc2, concgluc3,
concgluc4, concgrel, concinsu, conciron, conciron2, concneur, concneur2, concneur3, concneur4, concnitr, concnitr2, concotant, concotcor, concoth2, concothe, concpros,
concpsy5, concren, concresp, concrheu, concrheu2, concrheu3, concsta2, concstat, concthro, concthyr, concthyr2, concvit2, concvita, Contralateral_surgery, conwhen,
corticos, cortihorm2, creat, crp_all, CRP_avg, CRP_dif, crp_source, CRP_var, CST3_pg_ug, CST3_serum_luminex, CTGF, cTNI_plasma, CTSA, CTSB, CTSL1, CTSS, cyr61,
date_ic_patient, date_ic_researcher, Date.of.birth, date.previous.operation, date1yr, date3mon, dateapprox_latest, dateapprox_worst, dateapprox1, dateapprox2,
dateapprox3, dateapprox4, dateend1, dateend2, dateend3, dateend4, dateend5, dateend6, dateexact_latest, dateexact_worst, dateexact1, dateexact2, dateexact3, dateexact4,
dateok, dermacor, DiabetesStatus, diastoli, diet801, diet802, diet803, diet804, diet805, diet806, diet807, diet808, diet809, diet810, diet811, diet812, diet813,
diet814, diet815, diet816, diet817, diet818, diet819, diet820, diet821, diet822, diet823, diet824, dipyridi, diuretic, diuretic2, diuretic3, DM, DM.composite,
duaalantiplatelet, duplend, eaindexl, eaindexr, eCigarettes, edaplaqu_recalc, edavrspl, EGR, EMMPRIN_45kD, EMMPRIN_58kD, ENDOGLIN, endpoint1, endpoint2, endpoint3,
endpoint4, endpoint5, endpoint6, Eotaxin1, Eotaxin1_rank, EP_CAD, ep_cad_t_30days, ep_cad_t_3years, EP_CAD_time, ep_cad.30days, EP_CI, ep_ci_t_30days, ep_ci_t_3years,
EP_CI_time, ep_com_t_30days, ep_com_t_3years, EP_composite, EP_composite_time, EP_coronary, ep_coronary_t_30days, ep_coronary_t_3years, ep_coronary_t_90days,
EP_coronary_time, EP_CVdeath, ep_cvdeath_t_30days, ep_cvdeath_t_3years, ep_cvdeath_t_90days, EP_CVdeath_time, EP_death, ep_death_t_30days, ep_death_t_3years,
EP_death_time, EP_fatalCVA, ep_fatalCVA_t_30days, ep_fatalCVA_t_3years, EP_fatalCVA_time, EP_hemorrhagic_stroke, ep_hemorrhagic_stroke_t_3years,
EP_hemorrhagic_stroke_time, ep_hemorrhagic_stroke.3years, EP_ischemic_stroke, ep_ischemic_stroke_t_3years, EP_ischemic_stroke_time, ep_ischemic_stroke.3years,
EP_leg_amputation, EP_leg_amputation_time, ep_legamputation_t_30days, ep_legamputation_t_3years, EP_major, ep_major_t_30days, ep_major_t_3years, ep_major_t_90days,
EP_major_time, EP_MI, ep_mi_t_30days, ep_mi_t_3years, EP_MI_time, EP_nonstroke_event, EP_nonstroke_event_time, ep_nonstroke_t_3years, EP_peripheral,
ep_peripheral_t_30days, ep_peripheral_t_3years, EP_peripheral_time, EP_pta, ep_pta_t_30days, ep_pta_t_3years, EP_pta_time, EP_stroke, ep_stroke_t_30days,
ep_stroke_t_3years, ep_stroke_t_90days, EP_stroke_time, EP_strokeCVdeath, ep_strokeCVdeath_t_30days, ep_strokeCVdeath_t_3years, EP_strokeCVdeath_time, EP_strokedeath,
ep_strokedeath_t_30days, ep_strokedeath_t_3years, EP_strokedeath_time, ePackYearsSmoking, epcad.3years, epci.30days, epci.3years, epcom.30days, epcom.3years,
epcoronary.30days, epcoronary.3years, epcoronary.90days, epcvdeath.30days, epcvdeath.3years, epcvdeath.90days, epdeath.30days, epdeath.3years, epfatalCVA.30days,
epfatalCVA.3years, eplegamputation.30days, eplegamputation.3years, epmajor.30days, epmajor.3years, epmajor.90days, epmi.30days, epmi.3years, epnonstroke.3years,
epperipheral.30days, epperipheral.3years, eppta.30days, eppta.3years, epstroke.30days, epstroke.3years, epstroke.90days, epstrokeCVdeath.30days, epstrokeCVdeath.3years,
epstrokedeath.30days, epstrokedeath.3years, erec, Estradiol, everstroke_composite, Everstroke_Ipsilateral, exer901, exer902, exer903, exer904, exer905, exer906,
exer9071, exer9072, exer9073, exer9074, exer9075, exer9076, exer908, exer909, exer910, eyedrop, EZis, FABP_serum, FABP4, FABP4_pg_ug, FABP4_serum_luminex, fat,
Fat.bin_10, Fat.bin_40, Fat10Perc, Femoral.interv, FH_AAA_broth, FH_AAA_comp, FH_AAA_mat, FH_AAA_parent, FH_AAA_pat, FH_AAA_sibling, FH_AAA_sis, FH_amp_broth,
FH_amp_comp, FH_amp_mat, FH_amp_parent, FH_amp_pat, FH_amp_sibling, FH_amp_sis, FH_CAD_broth, FH_CAD_comp, FH_CAD_mat, FH_CAD_parent, FH_CAD_pat, FH_CAD_sibling,
FH_CAD_sis, FH_corcalc_broth, FH_corcalc_comp, FH_corcalc_mat, FH_corcalc_parent, FH_corcalc_pat, FH_corcalc_sibling, FH_corcalc_sis, FH_CVD_broth, FH_CVD_comp,
FH_CVD_mat, FH_CVD_parent, FH_CVD_pat, FH_CVD_sibling, FH_CVD_sis, FH_CVdeath_broth, FH_CVdeath_comp, FH_CVdeath_mat, FH_CVdeath_parent, FH_CVdeath_pat,
FH_CVdeath_sibling, FH_CVdeath_sis, FH_DM_broth, FH_DM_comp, FH_DM_mat, FH_DM_parent, FH_DM_pat, FH_DM_sibling, FH_DM_sis, FH_HC_broth, FH_HC_comp, FH_HC_mat,
FH_HC_parent, FH_HC_pat, FH_HC_sibling, FH_HC_sis, FH_HT_broth, FH_HT_comp, FH_HT_mat, FH_HT_parent, FH_HT_pat, FH_HT_sibling, FH_HT_sis, FH_MI_broth, FH_MI_comp,
FH_MI_mat, FH_MI_parent, FH_MI_pat, FH_MI_sibling, FH_MI_sis, FH_otherCVD_broth, FH_otherCVD_comp, FH_otherCVD_mat, FH_otherCVD_parent, FH_otherCVD_pat,
FH_otherCVD_sibling, FH_otherCVD_sis, FH_PAD_broth, FH_PAD_comp, FH_PAD_mat, FH_PAD_parent, FH_PAD_pat, FH_PAD_sibling, FH_PAD_sis, FH_PAV_broth, FH_PAV_comp,
FH_PAV_mat, FH_PAV_parent, FH_PAV_pat, FH_PAV_sibling, FH_PAV_sis, FH_POB_broth, FH_POB_comp, FH_POB_mat, FH_POB_parent, FH_POB_pat, FH_POB_sibling, FH_POB_sis,
FH_risk_broth, FH_risk_comp, FH_risk_mat, FH_risk_parent, FH_risk_pat, FH_risk_sibling, FH_risk_sis, FH_Stroke_broth, FH_Stroke_comp, FH_Stroke_mat, FH_Stroke_parent,
FH_Stroke_pat, FH_Stroke_sibling, FH_Stroke_sis, FH_tromb_broth, FH_tromb_comp, FH_tromb_mat, FH_tromb_parent, FH_tromb_pat, FH_tromb_sibling, FH_tromb_sis, filter_$,
folicaci, followup1, followup2, followup3, Fontaine, FU_check, FU_check_date, FU.cutt.off.30days, FU.cutt.off.3years, FU.cutt.off.90days, FU1JAAR, FU2JAAR, FU3JAAR,
FURIN_low, FURIN_up, GDF15_plasma, geen_med, Gender, GFR_CG, GFR_MDRD, glucose, GR_Segment, GrB_plaque, GrB_serum, grel, GrK_plaque, GrK_serum, GrM_plaque, GrM_serum,
HA, hb, HDAC9, HDL, HDL_2016, HDL_all, HDL_avg, HDL_clinic, HDL_dif, HDL_final, HDL_finalCU, hdl_source, HDL_var, heart300, heart301, heart302, heart303, heart304,
heart305, heart306, heart307, heart308, heart309, heart310, heart311, heart312, heart313, heart314, heart315, heart316, heart317, heart318, heart319, heart320,
heart321, heart322, heart323, heart324, heart325, heart326, heart327, heart328, HIF1A, ho1, homocys, Hospital, hrt31301, hsCRP_plasma, ht, HYAL55KD, HYALURON,
Hypertension.composite, Hypertension.drugs, Hypertension.selfreport, Hypertension.selfreportdrug, Hypertension1, Hypertension2, IL1_Beta, IL10, IL10_rank, IL12,
IL12_rank, IL13, IL13_rank, IL17, IL2, IL2_rank, IL21, IL21_rank, IL4, IL4_rank, IL5, IL5_rank, IL6, IL6_pg_ug_2015, IL6_rank, IL6R_pg_ug_2015, IL8, IL8_pg_ug_2015,
IL8_rank, IL9, IL9_rank, indexsymptoms_latest, indexsymptoms_latest_4g, indexsymptoms_worst, indexsymptoms_worst_4g, INFG, INFG_rank, informedconsent, insulin,
insuline, INVULDAT, IP10, IP10_rank, IPH, IPH_extended.bin, IPH.bin, ironfoli, ironfoli2, KDOQI, latest, LDL, LDL_2016, LDL_all, LDL_avg, LDL_clinic, LDL_dif,
LDL_final, LDL_finalCU, ldl_source, LDL_var, leg501, leg502, leg503, leg504, leg505, leg506, leg507, leg508, leg509, leg510, leg511, leg512, leg513, leg514, leg515,
leg516, leg517, leg518, leg519, leg520, LMW1STME, LTB4, LTB4R, macmean0, macrophages, Macrophages_LN, macrophages_location, Macrophages_rank, Macrophages.bin, MAP,
Mast_cells_plaque, max.followup, MCP1, MCP1_pg_ug_2015, MCP1_pg_ug_2015_LN, MCP1_pg_ug_2015_rank, MCP1_rank, MCSF_pg_ug_2015, MDC, MDC_rank, Med_notes, Med.ablock,
Med.ACE_inh, Med.acetylsal, Med.acetylsal_Combi1, Med.acetylsal_Combi2, Med.acetylsal_Combi3, Med.ADPinh, Med.all.antiplatelet, Med.angiot2.antag, Med.antiarrh,
Med.anticoagulants, Med.ascal, Med.aspirin.derived, Med.bblocker, Med.calc_antag, Med.dipyridamole, Med.diuretic, Med.LLD, Med.nitrate, Med.otheranthyp, Med.renin,
Med.statin, Med.statin.derived, Med.Statin.LLD, Med.statin2, MedHx_CVD, media, MG_H1, MI_Dx, MI_Dx1, MI_Dx2, MIF, MIF_rank, MIG, MIG_rank, MIP1a, MIP1a_rank,
miRNA100_RNU19, miRNA100_RNU48, miRNA155_RNU19, miRNA155_RNU48, MMP14, MMP2, MMP2_rank, MMP2TIMP2, MMP8, MMP8_rank, MMP9, MMP9_rank, MMP9TIMP1, MPO_plasma, MRP_14,
MRP_8, MRP_8_14C, MRP_8_14C_buhlmann, MRP14_plasma, MRP8_14C_plasma, MRP8_plasma, negatibl, neuropsy, neuropsy2, neuropsy3, neuropsy4, neurpsy5, neutrophils, NGAL,
NGAL_low, NGAL_MMP9_complex, NGAL_MMP9_local, NGAL_MMP9_peripheral, NGAL_total, NGAL_up, nitrate, nitrate2, NOD1, NOD2, nogobt1_recalculated, NTproBNP_plasma,
Number_Events_Sorter, Number_Sorted_CD14, Number_Sorted_CD20, Number_Sorted_CD4_Cells, Number_Sorted_CD8_Cells, oac701, oac702, oac70305, oac704, oac705, oac706,
oac707, oac708, oac709, oac710, oac711, oac712, oac713, oac714, OKyear, OPG, OPG_plasma, OPG_rank, OPN, OPN_2013, OPN_plasma, OR_blood, Oral.glucose.inh, oralgluc,
oralgluc2, oralgluc3, oralgluc4, ORyear, othanthyp, othcoron, other, other2, OverallPlaquePhenotype, PAI1_pg_ug_2015, PAOD, PARC, PARC_rank, patch, PCSK9_plasma,
PDGF_BB_plasma, Percentage_CD14, Percentage_CD20, Percentage_CD4, Percentage_CD8, Peripheral.interv, PKC, PLA2_plasma, plaquephenotype, positibl, PrimaryLast,
PrimaryLast1, prostagl, PulsePressure, qual01, qual02, qual0301, qual0302, qual0303, qual0304, qual0305, qual0306, qual0307, qual0308, qual0309, qual0310, qual0401,
qual0402, qual0403, qual0404, qual0501, qual0502, qual0503, qual06, qual07, qual08, qual0901, qual0902, qual0903, qual0904, qual0905, qual0906, qual0907, qual0908,
qual0909, qual1010, qual1101, qual1102, qual1103, qual1104, RAAS_med, RANTES, RANTES_pg_ug_2015, RANTES_plasma, RANTES_rank, Ras, RE50_01, RE70_01, Renine_recode,
renineinh, restenos, restenosisOK, rheuma, rheuma2, rheuma3, risk601, risk602, risk603, risk604, risk605, risk606, risk607, risk608, risk609, risk610, risk611, risk612,
risk613, risk614, risk615, risk616, risk617, risk618, risk619, risk620, SHBG, sICAM1, sICAM1_rank, SMAD1_5_8, SMAD2, SMAD3, smc, SMC_LN, smc_location,
smc_macrophages_ratio, SMC_rank, SMC.bin, smcmean0, SmokerCurrent, SmokerStatus, SmokingReported, SmokingYearOR, stat3P, statin2, statines, ste3mext, sten1yr, sten3mo,
stenose, stenosis_con_bin, Stenosis_contralateral, Stenosis_ipsilateral, Stroke_Dx, Stroke_eitherside, Stroke_history, Stroke_Symptoms, StrokeTIA_Dx, StrokeTIA_history,
StrokeTIA_Symptoms, STUDY_NUMBER, sympt, Sympt_latest, Sympt_worst, sympt1, sympt2, sympt3, sympt4, Symptoms.3g, Symptoms.4g, Symptoms.5G, systolic, T_NUMBER, TARC,
TARC_rank, TAT_plasma, TC_2016, TC_all, TC_avg, TC_clinic, TC_dif, TC_final, TC_finalCU, TC_var, Testosterone, TG_2016, TG_all, TG_avg, TG_clinic, TG_dif, TG_final,
TG_finalCU, TG_var, TGF, TGFB, TGFB_rank, thrombos, thrombus, thrombus_location, thrombus_new, thrombus_organization, thrombus_organization_v2, thrombus_percentage,
thyros2, thyrosta, Time_event_OR, TimeOR_latest, TimeOR_latest_4g, TimeOR_worst, TimeOR_worst_4g, TIMP1, TIMP2, TISNOW, TNFA, TNFA_rank, totalchol,
totalcholesterol_source, tractdig, tractdig2, tractdig3, tractdig4, tractres, Treatment.DM, TREM1, triglyceride_source, triglyceriden, Trop1, Trop1DT, Trop2, Trop2DT,
Trop3, Trop3DT, TropmaxpostOK, TropoMax, TropoMaxDT, tropomaxpositief, TSratio_blood, TSratio_plaque, UPID, validation_date, validation1, validation2, validation3,
validation4, validation5, validation6, VAR00001, VEGFA, VEGFA_plasma, VEGFA_rank, vegfa422, vessel_density, vessel_density_additional, vessel_density_averaged,
vessel_density_Timo2012, vessel_density_Timo2012_2, vessel_density_Timo2013, VesselDensity_LN, VesselDensity_rank, vitamin, vitamin2, vitb12, VRAGENLIJST, vWF_plasma,
WBC_THAW, Which.femoral.artery, Whichoperation, writtenIC, yearablo, yearablo2, yearablo3, yearace, yearace2, yearacet, yearanal, yearanal2, yearanal3, yearangi,
yearanta, yearanta2, yearanti, yearanti2, yearbblo, yearbblo2, yearcalc, yearcalc2, yearcalreg, yearcarb, yearchol, yearchol2, yearchol3, yearclau1, yearclau2,
yearclop, yearcom1, yearcom2, yearcom3, yearcort, yearcorthorm2, yearderm, yeardig, yeardig2, yeardig3, yeardig4, yeardipy, yeardiur, yeardiur2, yeardiur3, yearerec,
yeareye, yeargluc, yeargluc2, yeargluc3, yeargluc4, yeargrel, yearinsu, yeariron, yeariron2, yearneur, yearneur2, yearneur3, yearneur4, yearnitr, yearnitr2,
yearOR_bin_2010, YearOR_per2years, yearotant, yearotcor, yearoth2, yearothe, yearpros, yearpsy5, yearren, yearresp, yearrheu, yearrheu2, yearrheu3, yearsta2, yearstat,
yearthro, yearthyr, yearthyr2, yearvit2, yearvita, Yrs.no.smoking, Yrs.smokingAEDB.CEA$yeartemp <- as.numeric(year(AEDB.CEA$dateok))
AEDB.CEA[,"ORyearGroup"] <- NA
AEDB.CEA$ORyearGroup[yeartemp <= 2007] <- "< 2007"
AEDB.CEA$ORyearGroup[yeartemp > 2007] <- "> 2007"
detach(AEDB.CEA)
table(AEDB.CEA$ORyearGroup, AEDB.CEA$ORdate_year)
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
< 2007 81 157 190 185 183 152 0 0 0 0 0 0 0 0 0 0 0 0
> 2007 0 0 0 0 0 0 138 182 159 164 176 149 163 76 85 65 66 52# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Plaque_Vulnerability_Index",
y = "MCP1_pg_ug_2015_rank",
xlab = "Plaque vulnerability index",
ylab = "MCP1 plaque [pg/ug]\n(INT, exp 2)",
color = "Plaque_Vulnerability_Index",
palette = "npg",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Plaque_Vulnerability_Index",
y = "MCP1_pg_ml_2015_rank",
xlab = "Plaque vulnerability index",
ylab = "MCP1 plaque [pg/mL]\n(INT, exp 2)",
color = "Plaque_Vulnerability_Index",
palette = "npg",
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Plaque vulnerability index")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p3 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Plaque_Vulnerability_Index",
y = "MCP1_rank",
xlab = "Plaque vulnerability index",
ylab = "MCP1 plaque [pg/mL]\n(INT, exp 1)",
color = "Plaque_Vulnerability_Index",
palette = "npg",
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p3, legend = "bottom", legend.title = "Plaque vulnerability index")
p1 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Plaque_Vulnerability_Index",
y = "MCP1_pg_ug_2015_rank",
xlab = "Plaque vulnerability index",
ylab = "MCP1 plaque [pg/ug]\n(INT, exp 2)",
color = "Plaque_Vulnerability_Index",
palette = "npg",
facet.by = "ORyearGroup",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Plaque_Vulnerability_Index",
y = "MCP1_pg_ml_2015_rank",
xlab = "Plaque vulnerability index",
ylab = "MCP1 plaque [pg/mL]\n(INT, exp 2)",
color = "Plaque_Vulnerability_Index",
palette = "npg",
facet.by = "ORyearGroup",
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Plaque vulnerability index")
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p3 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Plaque_Vulnerability_Index",
y = "MCP1_rank",
xlab = "Plaque vulnerability index",
ylab = "MCP1 plaque [pg/mL]\n(INT, exp 1)",
color = "Plaque_Vulnerability_Index",
palette = "npg",
facet.by = "ORyearGroup",
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p3, legend = "bottom", legend.title = "Plaque vulnerability index")
Model 1
In this model we correct for Age, Gender, and year of surgery.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of the plaque vulnerability indez as a function of plasma/plaque MCP1 levels.
TRAITS.PROTEIN.RANK.extra = c("MCP1_pg_ug_2015_rank", "MCP1_pg_ml_2015_rank", "MCP1_rank")
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK.extra)) {
PROTEIN = TRAITS.PROTEIN.RANK.extra[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "Plaque_Vulnerability_Index"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1, ORdate_epoch) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
# table(currentDF$ORdate_year)
### univariate
# + Hypertension.composite + DiabetesStatus + SmokerCurrent +
# Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
# CAD_history + Stroke_history + Peripheral.interv + stenose
fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year,
data = currentDF,
Hess = TRUE)
# model_step <- stepAIC(fit, direction = "both", trace = FALSE)
# print(model_step)
print(summary(fit))
## store table
(ctable <- coef(summary(fit)))
## Value Std. Error t value
## pared 1.04769 0.2658 3.9418
## public -0.05879 0.2979 -0.1974
## gpa 0.61594 0.2606 2.3632
## unlikely|somewhat likely 2.20391 0.7795 2.8272
## somewhat likely|very likely 4.29936 0.8043 5.3453
## calculate and store p values
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
## combined table
print((ctable <- cbind(ctable, "p value" = p)))
}
Analysis of MCP1_pg_ug_2015_rank.
- processing Plaque_Vulnerability_Indexdesign appears to be rank-deficient, so dropping some coefsCall:
polr(formula = currentDF[, TRAIT] ~ currentDF[, PROTEIN] + Age +
Gender + ORdate_year, data = currentDF, Hess = TRUE)
Coefficients:
Value Std. Error t value
currentDF[, PROTEIN] 0.3057 0.058114 5.2602
Age 0.0121 0.005715 2.1179
Gendermale 0.7740 0.114391 6.7663
ORdate_year2003 0.4959 0.296359 1.6732
ORdate_year2004 0.5485 0.288135 1.9037
ORdate_year2005 0.4627 0.290954 1.5903
ORdate_year2006 0.3265 0.289142 1.1292
ORdate_year2007 0.3705 0.288504 1.2843
ORdate_year2008 -0.5643 0.308477 -1.8293
ORdate_year2009 -0.4251 0.306478 -1.3870
ORdate_year2010 -0.5996 0.306899 -1.9536
ORdate_year2011 -0.9460 0.298813 -3.1659
ORdate_year2012 -0.7192 0.306270 -2.3484
ORdate_year2013 -3.4684 0.551562 -6.2883
ORdate_year2014 0.2310 1.543815 0.1496
Intercepts:
Value Std. Error t value
0|1 -1.2832 0.4676 -2.7445
1|2 0.2046 0.4612 0.4437
2|3 1.4286 0.4627 3.0875
3|4 2.9913 0.4691 6.3761
4|5 4.7160 0.4889 9.6466
Residual Deviance: 3724.532
AIC: 3764.532
Value Std. Error t value p value
currentDF[, PROTEIN] 0.30569322 0.058114324 5.2602044 1.438954e-07
Age 0.01210316 0.005714604 2.1179347 3.418060e-02
Gendermale 0.77399704 0.114390587 6.7662651 1.321493e-11
ORdate_year2003 0.49585913 0.296359468 1.6731678 9.429426e-02
ORdate_year2004 0.54851725 0.288135147 1.9036805 5.695181e-02
ORdate_year2005 0.46270314 0.290953970 1.5902967 1.117679e-01
ORdate_year2006 0.32649166 0.289141566 1.1291758 2.588237e-01
ORdate_year2007 0.37053291 0.288503596 1.2843268 1.990276e-01
ORdate_year2008 -0.56430545 0.308477270 -1.8293259 6.735081e-02
ORdate_year2009 -0.42509922 0.306478054 -1.3870462 1.654277e-01
ORdate_year2010 -0.59956082 0.306899333 -1.9536074 5.074766e-02
ORdate_year2011 -0.94599990 0.298813488 -3.1658541 1.546283e-03
ORdate_year2012 -0.71923380 0.306269979 -2.3483653 1.885602e-02
ORdate_year2013 -3.46837812 0.551561850 -6.2882850 3.209922e-10
ORdate_year2014 0.23099155 1.543815040 0.1496239 8.810614e-01
0|1 -1.28324430 0.467560971 -2.7445496 6.059399e-03
1|2 0.20464146 0.461248185 0.4436689 6.572820e-01
2|3 1.42858169 0.462698670 3.0874990 2.018484e-03
3|4 2.99126034 0.469133681 6.3761364 1.816112e-10
4|5 4.71603386 0.488882868 9.6465517 5.083645e-22
Analysis of MCP1_pg_ml_2015_rank.
- processing Plaque_Vulnerability_Indexdesign appears to be rank-deficient, so dropping some coefsCall:
polr(formula = currentDF[, TRAIT] ~ currentDF[, PROTEIN] + Age +
Gender + ORdate_year, data = currentDF, Hess = TRUE)
Coefficients:
Value Std. Error t value
currentDF[, PROTEIN] 0.49640 0.061426 8.08130
Age 0.01055 0.005737 1.83860
Gendermale 0.64459 0.115868 5.56312
ORdate_year2003 0.56040 0.296325 1.89117
ORdate_year2004 0.66559 0.287471 2.31532
ORdate_year2005 0.51828 0.288266 1.79792
ORdate_year2006 0.22049 0.286933 0.76843
ORdate_year2007 0.30253 0.287285 1.05307
ORdate_year2008 -0.75110 0.308156 -2.43742
ORdate_year2009 -0.62203 0.308254 -2.01793
ORdate_year2010 -0.72838 0.307038 -2.37229
ORdate_year2011 -1.18831 0.301396 -3.94270
ORdate_year2012 -0.97388 0.306639 -3.17600
ORdate_year2013 -3.79678 0.571861 -6.63934
ORdate_year2014 0.13910 1.547820 0.08987
Intercepts:
Value Std. Error t value
0|1 -1.6004 0.4694 -3.4092
1|2 -0.0928 0.4625 -0.2006
2|3 1.1629 0.4634 2.5097
3|4 2.7553 0.4694 5.8702
4|5 4.4926 0.4887 9.1935
Residual Deviance: 3690.587
AIC: 3730.587
Value Std. Error t value p value
currentDF[, PROTEIN] 0.49640303 0.061426118 8.08130238 6.407870e-16
Age 0.01054841 0.005737182 1.83860451 6.597338e-02
Gendermale 0.64458944 0.115868286 5.56312219 2.649902e-08
ORdate_year2003 0.56040215 0.296325240 1.89117252 5.860132e-02
ORdate_year2004 0.66558719 0.287470504 2.31532342 2.059524e-02
ORdate_year2005 0.51828038 0.288266450 1.79792128 7.218948e-02
ORdate_year2006 0.22048748 0.286932533 0.76842970 4.422319e-01
ORdate_year2007 0.30253246 0.287284814 1.05307500 2.923066e-01
ORdate_year2008 -0.75110315 0.308155608 -2.43741515 1.479269e-02
ORdate_year2009 -0.62203318 0.308253755 -2.01792573 4.359900e-02
ORdate_year2010 -0.72838346 0.307038066 -2.37229042 1.767819e-02
ORdate_year2011 -1.18831404 0.301395943 -3.94270085 8.056913e-05
ORdate_year2012 -0.97388411 0.306638573 -3.17600002 1.493209e-03
ORdate_year2013 -3.79677843 0.571860557 -6.63934308 3.150842e-11
ORdate_year2014 0.13909661 1.547820408 0.08986612 9.283936e-01
0|1 -1.60038302 0.469430932 -3.40919805 6.515416e-04
1|2 -0.09276549 0.462476705 -0.20058414 8.410238e-01
2|3 1.16294943 0.463373351 2.50974604 1.208180e-02
3|4 2.75528554 0.469364823 5.87024293 4.351570e-09
4|5 4.49257395 0.488668440 9.19350134 3.802580e-20
Analysis of MCP1_rank.
- processing Plaque_Vulnerability_Indexdesign appears to be rank-deficient, so dropping some coefsCall:
polr(formula = currentDF[, TRAIT] ~ currentDF[, PROTEIN] + Age +
Gender + ORdate_year, data = currentDF, Hess = TRUE)
Coefficients:
Value Std. Error t value
currentDF[, PROTEIN] 0.57203 0.084229 6.791
Age 0.01808 0.009025 2.003
Gendermale 0.67365 0.173664 3.879
ORdate_year2003 0.43422 0.277352 1.566
ORdate_year2004 0.39935 0.267208 1.495
ORdate_year2005 0.29320 0.267175 1.097
ORdate_year2006 0.55501 0.416024 1.334
Intercepts:
Value Std. Error t value
0|1 -1.3622 0.6784 -2.0080
1|2 0.3669 0.6571 0.5583
2|3 1.7271 0.6595 2.6188
3|4 3.3951 0.6713 5.0572
4|5 5.0232 0.6926 7.2529
Residual Deviance: 1678.934
AIC: 1702.934
Value Std. Error t value p value
currentDF[, PROTEIN] 0.57202541 0.084228722 6.7913343 1.111012e-11
Age 0.01808048 0.009024701 2.0034439 4.512966e-02
Gendermale 0.67365410 0.173664343 3.8790583 1.048616e-04
ORdate_year2003 0.43421860 0.277352217 1.5655855 1.174457e-01
ORdate_year2004 0.39935278 0.267208177 1.4945380 1.350350e-01
ORdate_year2005 0.29319541 0.267175436 1.0973891 2.724713e-01
ORdate_year2006 0.55501313 0.416023652 1.3340903 1.821743e-01
0|1 -1.36218766 0.678391513 -2.0079668 4.464682e-02
1|2 0.36690940 0.657133562 0.5583483 5.766066e-01
2|3 1.72706246 0.659490126 2.6187844 8.824370e-03
3|4 3.39510771 0.671343089 5.0571872 4.254854e-07
4|5 5.02318542 0.692576136 7.2528999 4.079414e-13Model 2
In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis..
for (protein in 1:length(TRAITS.PROTEIN.RANK.extra)) {
PROTEIN = TRAITS.PROTEIN.RANK.extra[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "Plaque_Vulnerability_Index"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose,
data = currentDF,
Hess = TRUE)
print(summary(fit))
## store table
(ctable <- coef(summary(fit)))
## calculate and store p values
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
## combined table
print((ctable <- cbind(ctable, "p value" = p)))
}
Analysis of MCP1_pg_ug_2015_rank.
- processing Plaque_Vulnerability_Indexdesign appears to be rank-deficient, so dropping some coefsCall:
polr(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF, Hess = TRUE)
Coefficients:
Value Std. Error t value
currentDF[, PROTEIN] 0.305332 0.063077 4.84061
Age 0.007468 0.007140 1.04592
Gendermale 0.785610 0.128340 6.12132
ORdate_year2003 0.515957 0.312661 1.65021
ORdate_year2004 0.610638 0.302574 2.01814
ORdate_year2005 0.540695 0.306925 1.76165
ORdate_year2006 0.321004 0.308511 1.04049
ORdate_year2007 0.346536 0.314148 1.10310
ORdate_year2008 -0.525459 0.336624 -1.56097
ORdate_year2009 -0.345325 0.327635 -1.05399
ORdate_year2010 -0.577428 0.322771 -1.78897
ORdate_year2011 -1.017953 0.317861 -3.20251
ORdate_year2012 -1.022643 0.342484 -2.98596
ORdate_year2013 -3.212987 0.694778 -4.62448
Hypertension.compositeno 0.133924 0.174974 0.76539
DiabetesStatusDiabetes -0.189752 0.138801 -1.36708
SmokerStatusEx-smoker 0.058149 0.129516 0.44897
SmokerStatusNever smoked 0.495909 0.184731 2.68450
Med.Statin.LLDno -0.049707 0.138595 -0.35865
Med.all.antiplateletno 0.027994 0.198478 0.14104
GFR_MDRD -0.002442 0.003021 -0.80810
BMI 0.001411 0.015807 0.08929
MedHx_CVDyes 0.193584 0.119075 1.62573
stenose0-49% 0.367682 0.936920 0.39244
stenose50-70% -0.415337 0.702024 -0.59163
stenose70-90% -0.373459 0.673734 -0.55431
stenose90-99% -0.511653 0.675446 -0.75750
stenose100% (Occlusion) -1.116142 0.883883 -1.26277
stenose50-99% -1.080046 1.014918 -1.06417
Intercepts:
Value Std. Error t value
0|1 -2.0282 1.0478 -1.9356
1|2 -0.4682 1.0435 -0.4487
2|3 0.7706 1.0442 0.7380
3|4 2.3476 1.0471 2.2419
4|5 4.0284 1.0565 3.8130
Residual Deviance: 3213.842
AIC: 3281.842
Value Std. Error t value p value
currentDF[, PROTEIN] 0.305331731 0.063077082 4.84061280 1.294394e-06
Age 0.007467756 0.007139897 1.04591929 2.955983e-01
Gendermale 0.785609855 0.128340045 6.12131510 9.280617e-10
ORdate_year2003 0.515957154 0.312660748 1.65021403 9.889917e-02
ORdate_year2004 0.610638292 0.302574362 2.01814287 4.357638e-02
ORdate_year2005 0.540695474 0.306925483 1.76165064 7.812834e-02
ORdate_year2006 0.321003691 0.308510969 1.04049361 2.981106e-01
ORdate_year2007 0.346535921 0.314147617 1.10309900 2.699842e-01
ORdate_year2008 -0.525459304 0.336623918 -1.56096842 1.185312e-01
ORdate_year2009 -0.345324946 0.327634807 -1.05399347 2.918859e-01
ORdate_year2010 -0.577427919 0.322770600 -1.78897310 7.361915e-02
ORdate_year2011 -1.017953362 0.317860938 -3.20251167 1.362348e-03
ORdate_year2012 -1.022643347 0.342483801 -2.98596122 2.826885e-03
ORdate_year2013 -3.212987260 0.694777901 -4.62448108 3.755374e-06
Hypertension.compositeno 0.133923508 0.174973718 0.76539214 4.440381e-01
DiabetesStatusDiabetes -0.189752383 0.138800809 -1.36708413 1.715989e-01
SmokerStatusEx-smoker 0.058148532 0.129516298 0.44896691 6.534555e-01
SmokerStatusNever smoked 0.495909396 0.184730765 2.68449814 7.263879e-03
Med.Statin.LLDno -0.049706679 0.138594750 -0.35864763 7.198587e-01
Med.all.antiplateletno 0.027993676 0.198477844 0.14104182 8.878369e-01
GFR_MDRD -0.002441513 0.003021313 -0.80809692 4.190348e-01
BMI 0.001411415 0.015807418 0.08928813 9.288529e-01
MedHx_CVDyes 0.193583571 0.119074889 1.62572959 1.040072e-01
stenose0-49% 0.367682230 0.936920041 0.39243715 6.947352e-01
stenose50-70% -0.415336580 0.702024088 -0.59162725 5.541002e-01
stenose70-90% -0.373458653 0.673734134 -0.55431161 5.793656e-01
stenose90-99% -0.511653424 0.675445610 -0.75750499 4.487474e-01
stenose100% (Occlusion) -1.116142250 0.883882619 -1.26277203 2.066711e-01
stenose50-99% -1.080046443 1.014917743 -1.06417141 2.872511e-01
0|1 -2.028207198 1.047847709 -1.93559348 5.291751e-02
1|2 -0.468222521 1.043507451 -0.44870070 6.536476e-01
2|3 0.770592951 1.044187456 0.73798334 4.605246e-01
3|4 2.347571838 1.047139411 2.24189044 2.496845e-02
4|5 4.028422633 1.056510469 3.81295099 1.373174e-04
Analysis of MCP1_pg_ml_2015_rank.
- processing Plaque_Vulnerability_Indexdesign appears to be rank-deficient, so dropping some coefsCall:
polr(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF, Hess = TRUE)
Coefficients:
Value Std. Error t value
currentDF[, PROTEIN] 0.501879 0.066104 7.59232
Age 0.005080 0.007145 0.71100
Gendermale 0.669401 0.129803 5.15706
ORdate_year2003 0.568633 0.312735 1.81826
ORdate_year2004 0.710219 0.301668 2.35430
ORdate_year2005 0.582196 0.304527 1.91180
ORdate_year2006 0.213520 0.306856 0.69583
ORdate_year2007 0.291046 0.313525 0.92830
ORdate_year2008 -0.713068 0.336418 -2.11959
ORdate_year2009 -0.558510 0.330338 -1.69072
ORdate_year2010 -0.719264 0.322961 -2.22709
ORdate_year2011 -1.287328 0.321384 -4.00557
ORdate_year2012 -1.255026 0.343327 -3.65549
ORdate_year2013 -3.495730 0.710111 -4.92280
Hypertension.compositeno 0.145392 0.174904 0.83127
DiabetesStatusDiabetes -0.186962 0.138971 -1.34533
SmokerStatusEx-smoker 0.059708 0.129523 0.46099
SmokerStatusNever smoked 0.514448 0.184561 2.78741
Med.Statin.LLDno -0.083931 0.139279 -0.60261
Med.all.antiplateletno 0.036630 0.198318 0.18470
GFR_MDRD -0.003125 0.003022 -1.03391
BMI 0.001107 0.015919 0.06957
MedHx_CVDyes 0.212898 0.119093 1.78766
stenose0-49% 0.298906 0.942521 0.31713
stenose50-70% -0.337825 0.712555 -0.47410
stenose70-90% -0.341555 0.684308 -0.49913
stenose90-99% -0.458620 0.685690 -0.66884
stenose100% (Occlusion) -1.081223 0.893424 -1.21020
stenose50-99% -0.960389 1.021974 -0.93974
Intercepts:
Value Std. Error t value
0|1 -2.4167 1.0566 -2.2873
1|2 -0.8260 1.0518 -0.7854
2|3 0.4480 1.0520 0.4258
3|4 2.0572 1.0546 1.9507
4|5 3.7523 1.0636 3.5278
Residual Deviance: 3178.605
AIC: 3246.605
Value Std. Error t value p value
currentDF[, PROTEIN] 0.501879199 0.066103565 7.59231663 3.142360e-14
Age 0.005080065 0.007144933 0.71100239 4.770828e-01
Gendermale 0.669400843 0.129802923 5.15705524 2.508638e-07
ORdate_year2003 0.568632853 0.312735426 1.81825532 6.902512e-02
ORdate_year2004 0.710219343 0.301668437 2.35430445 1.855740e-02
ORdate_year2005 0.582196051 0.304526901 1.91180500 5.590121e-02
ORdate_year2006 0.213520140 0.306856142 0.69583140 4.865344e-01
ORdate_year2007 0.291045990 0.313524522 0.92830375 3.532500e-01
ORdate_year2008 -0.713068306 0.336418027 -2.11959006 3.404063e-02
ORdate_year2009 -0.558509819 0.330338382 -1.69072033 9.089023e-02
ORdate_year2010 -0.719264370 0.322960798 -2.22709497 2.594093e-02
ORdate_year2011 -1.287327723 0.321384309 -4.00557117 6.186780e-05
ORdate_year2012 -1.255026254 0.343326787 -3.65548597 2.566952e-04
ORdate_year2013 -3.495729585 0.710110642 -4.92279566 8.531653e-07
Hypertension.compositeno 0.145392288 0.174903932 0.83126940 4.058215e-01
DiabetesStatusDiabetes -0.186962156 0.138971294 -1.34532931 1.785189e-01
SmokerStatusEx-smoker 0.059708447 0.129523089 0.46098689 6.448080e-01
SmokerStatusNever smoked 0.514448240 0.184561476 2.78740857 5.313144e-03
Med.Statin.LLDno -0.083930727 0.139279305 -0.60260731 5.467700e-01
Med.all.antiplateletno 0.036629710 0.198318355 0.18470156 8.534631e-01
GFR_MDRD -0.003124921 0.003022440 -1.03390659 3.011798e-01
BMI 0.001107444 0.015919188 0.06956661 9.445386e-01
MedHx_CVDyes 0.212897870 0.119093290 1.78765630 7.383148e-02
stenose0-49% 0.298906012 0.942521186 0.31713453 7.511415e-01
stenose50-70% -0.337824920 0.712555374 -0.47410339 6.354262e-01
stenose70-90% -0.341555425 0.684308239 -0.49912511 6.176912e-01
stenose90-99% -0.458619919 0.685690201 -0.66884421 5.035949e-01
stenose100% (Occlusion) -1.081222983 0.893423835 -1.21020163 2.262015e-01
stenose50-99% -0.960389100 1.021974086 -0.93973919 3.473514e-01
0|1 -2.416738740 1.056584061 -2.28731327 2.217754e-02
1|2 -0.826027935 1.051769362 -0.78536984 4.322368e-01
2|3 0.447979044 1.052036011 0.42582102 6.702383e-01
3|4 2.057219388 1.054616979 1.95067918 5.109522e-02
4|5 3.752259497 1.063624011 3.52780631 4.190186e-04
Analysis of MCP1_rank.
- processing Plaque_Vulnerability_Indexdesign appears to be rank-deficient, so dropping some coefsCall:
polr(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF, Hess = TRUE)
Coefficients:
Value Std. Error t value
currentDF[, PROTEIN] 0.586123 0.089511 6.5481
Age 0.008787 0.010785 0.8147
Gendermale 0.744762 0.190135 3.9170
ORdate_year2003 0.449140 0.289888 1.5494
ORdate_year2004 0.457787 0.278941 1.6412
ORdate_year2005 0.352510 0.284574 1.2387
ORdate_year2006 0.552819 0.458885 1.2047
Hypertension.compositeno -0.315683 0.250322 -1.2611
DiabetesStatusDiabetes -0.237524 0.213716 -1.1114
SmokerStatusEx-smoker -0.096135 0.183641 -0.5235
SmokerStatusNever smoked 0.321597 0.276485 1.1632
Med.Statin.LLDno -0.160459 0.192813 -0.8322
Med.all.antiplateletno 0.134754 0.300892 0.4478
GFR_MDRD -0.003760 0.004595 -0.8185
BMI 0.035757 0.021837 1.6375
MedHx_CVDyes 0.209727 0.173756 1.2070
stenose0-49% -0.921033 1.218981 -0.7556
stenose50-70% -0.902415 0.865043 -1.0432
stenose70-90% -0.208182 0.754066 -0.2761
stenose90-99% -0.348934 0.755019 -0.4622
Intercepts:
Value Std. Error t value
0|1 -1.5450 1.3062 -1.1828
1|2 0.1986 1.2938 0.1535
2|3 1.6251 1.2962 1.2538
3|4 3.2419 1.3021 2.4898
4|5 4.8716 1.3124 3.7121
Residual Deviance: 1501.591
AIC: 1551.591
Value Std. Error t value p value
currentDF[, PROTEIN] 0.586123426 0.089510862 6.5480704 5.828524e-11
Age 0.008787238 0.010785201 0.8147496 4.152157e-01
Gendermale 0.744762316 0.190135350 3.9170113 8.965355e-05
ORdate_year2003 0.449139576 0.289887680 1.5493572 1.212959e-01
ORdate_year2004 0.457786932 0.278940674 1.6411623 1.007637e-01
ORdate_year2005 0.352510344 0.284573594 1.2387317 2.154449e-01
ORdate_year2006 0.552819029 0.458884551 1.2047018 2.283185e-01
Hypertension.compositeno -0.315683361 0.250322466 -1.2611068 2.072704e-01
DiabetesStatusDiabetes -0.237523572 0.213715543 -1.1114005 2.663960e-01
SmokerStatusEx-smoker -0.096135041 0.183640904 -0.5234947 6.006300e-01
SmokerStatusNever smoked 0.321597474 0.276485120 1.1631638 2.447631e-01
Med.Statin.LLDno -0.160459149 0.192813267 -0.8321997 4.052962e-01
Med.all.antiplateletno 0.134753622 0.300892110 0.4478470 6.542636e-01
GFR_MDRD -0.003760483 0.004594556 -0.8184649 4.130918e-01
BMI 0.035757146 0.021836549 1.6374907 1.015280e-01
MedHx_CVDyes 0.209727360 0.173755505 1.2070257 2.274222e-01
stenose0-49% -0.921032868 1.218981332 -0.7555759 4.499035e-01
stenose50-70% -0.902414539 0.865043004 -1.0432019 2.968548e-01
stenose70-90% -0.208181570 0.754066447 -0.2760785 7.824877e-01
stenose90-99% -0.348933790 0.755019447 -0.4621521 6.439723e-01
0|1 -1.544977556 1.306240128 -1.1827669 2.369016e-01
1|2 0.198563329 1.293847368 0.1534674 8.780297e-01
2|3 1.625147067 1.296152141 1.2538243 2.099059e-01
3|4 3.241937134 1.302078734 2.4898165 1.278091e-02
4|5 4.871622600 1.312376782 3.7120609 2.055785e-04Comparison sample types OLINK-platform
We performed a pilot experiment comparing plasma and plaque-derived protein levels as measured using the OLINK platform.
My colleague, Arjan Boltjes, analyzed this. Below some graphs and some statistics.
estimate: -0.0004093809 statistic: -0.003774306 p.value: 0.9969974 parameter: 85 conf.low: -0.2110394 conf.high: 0.210257 method: Pearson’s product-moment correlation alternative: two.sided
Figure 1: Distributions of plaque and plasma MCP1 levels. Measured using the OLINK-platform (CVD-III panel). Pilot experiment with n = 88 samples. 
Figure 2: Comparison of plaque and plasma MCP1 levels. Measured using the OLINK-platform (CVD-III panel). Pilot experiment with n = 88 samples. AU = Arbitrary unit. 
Session information
Version: v1.0.12
Last update: 2020-07-06
Written by: Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description: Script to analyse MCP1 from the Ather-Express Biobank Study.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
**MoSCoW To-Do List**
The things we Must, Should, Could, and Would have given the time we have.
_M_
* ASAP - analysis on plasma based on OLINK platform
* DONE - analysis on the pilot dataset on the OLINK platform, comparing plasma vs. plaque
* DONE - linear regression models (model 1 and model 2) of `MCP1_pg_ug_2015` with cytokines
* DONE - check out the difference between the measuremens of `MCP1` and `MCP1_pg_ug_2015` > `MCP1_pg_ug_2015` and `MCP1_pg_ml_2015` give similar results, `MCP1_pg_ug_2015` is more correct as this is corrected for the total amount of protein in the protein-sample used for the measurement.
* DONE - double check the plotting of the MACE
* DONE - add the statistics for the correlation of `MCP1_pg_ug_2015` with the cytokines
* DONE - add the comparison between `MCP1`, `MCP1_pg_ml_2015`, and `MCP1_pg_ug_2015`
* DONE - analysis in the context of year of surgery given Van Lammeren _et al._
* DONE - add analysis on vulnerability index
* DONE - add analysis on binary and ordinal plaque phenotypes
* DONE - add boxplots of MCP1 levels stratified by confounders/variables
_S_
* DONE prettify forest plot
_C_
_W_
**Changes log**
* v1.0.12 Add boxplots of MCP1 levels stratified by confounder/variables.
* v1.0.11 Add analysis of pilot data comparing OLINK-platform based MCP1 levels in plasma and plaque.
* v1.0.10 Add analyses for all three `MCP1`, `MCP1_pg_ml_2015`, and `MCP1_pg_ug_2015`. Add comparison between `MCP1`, `MCP1_pg_ml_2015`, and `MCP1_pg_ug_2015`. Add (and fixed) ordinal regression. Double checked which measurement to use.
* v1.0.9 Added linear regression models for MCP1 vs. cytokines plaque levels. Double checked upload of MACE-plots. Added statistics from correlation (heatmap) to txt-file.
* v1.0.8 Fixed error in MCP1 plasma analysis. It turns out the `MCP1` and `MCP1_pg_ug_2015` variables are _both_ measured in plaque, in two separate experiments, exp. no. 1 and exp. no. 2, respectively.
* v1.0.7 Fixed the per Age-group MCP1 Box plots. Added correlations with other cytokines in plaques.
* v1.0.6 Only analyses and figures that end up in the final manuscript.
* v1.0.5 Update with 30- and 90-days survival.
* v1.0.4 Updated with Cox-regressions.
* v1.0.3 Included more models.
* v1.0.2 Bugs fixed.
* v1.0.1 Extended with linear and logistic regressions.
* v1.0.0 Inital version.sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin19.4.0 (64-bit)
Running under: macOS Catalina 10.15.5
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /usr/local/Cellar/openblas/0.3.10/lib/libopenblasp-r0.3.10.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] tools stats graphics grDevices utils datasets methods base
other attached packages:
[1] GGally_1.5.0 PerformanceAnalytics_2.0.4 xts_0.12-0 zoo_1.8-8 patchwork_1.0.0.9000 Hmisc_4.4-0
[7] Formula_1.2-3 lattice_0.20-41 survminer_0.4.6 survival_3.1-12 openxlsx_4.1.5 ggcorrplot_0.1.3.999
[13] ggpubr_0.3.0 tableone_0.11.1 labelled_2.4.0 sjPlot_2.8.4 sjlabelled_1.1.5 haven_2.3.0
[19] devtools_2.3.0 usethis_1.6.1 MASS_7.3-51.6 DT_0.13 knitr_1.28 forcats_0.5.0
[25] stringr_1.4.0 purrr_0.3.4 tibble_3.0.1 ggplot2_3.3.0 tidyverse_1.3.0 data.table_1.12.8
[31] naniar_0.5.1 tidyr_1.1.0 dplyr_0.8.5 optparse_1.6.6 readr_1.3.1
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.1.7 plyr_1.8.6 splines_3.6.3 crosstalk_1.1.0.1 TH.data_1.0-10 inline_0.3.15 digest_0.6.25
[9] htmltools_0.4.0 fansi_0.4.1 checkmate_2.0.0 magrittr_1.5 memoise_1.1.0 cluster_2.1.0 remotes_2.1.1 modelr_0.1.8
[17] matrixStats_0.56.0 sandwich_2.5-1 prettyunits_1.1.1 jpeg_0.1-8.1 colorspace_1.4-1 rvest_0.3.5 mitools_2.4 xfun_0.14
[25] callr_3.4.3 crayon_1.3.4 jsonlite_1.6.1 lme4_1.1-23 glue_1.4.1 gtable_0.3.0 emmeans_1.4.7 sjstats_0.18.0
[33] sjmisc_2.8.4 car_3.0-8 pkgbuild_1.0.8 rstan_2.19.3 abind_1.4-5 scales_1.1.1 mvtnorm_1.1-0 DBI_1.1.0
[41] rstatix_0.5.0.999 ggeffects_0.14.3 Rcpp_1.0.4.6 htmlTable_1.13.3 xtable_1.8-4 performance_0.4.6 foreign_0.8-75 km.ci_0.5-2
[49] StanHeaders_2.19.2 stats4_3.6.3 survey_4.0 htmlwidgets_1.5.1 httr_1.4.1 getopt_1.20.3 RColorBrewer_1.1-2 acepack_1.4.1
[57] ellipsis_0.3.1 reshape_0.8.8 loo_2.2.0 pkgconfig_2.0.3 farver_2.0.3 nnet_7.3-14 dbplyr_1.4.3 tidyselect_1.1.0
[65] labeling_0.3 rlang_0.4.6 reshape2_1.4.4 effectsize_0.3.1 munsell_0.5.0 cellranger_1.1.0 cli_2.0.2 generics_0.0.2
[73] broom_0.5.6 evaluate_0.14 yaml_2.2.1 processx_3.4.2 fs_1.4.1 zip_2.0.4 survMisc_0.5.5 packrat_0.5.0
[81] visdat_0.5.3 nlme_3.1-148 xml2_1.3.2 compiler_3.6.3 rstudioapi_0.11 png_0.1-7 curl_4.3 e1071_1.7-3
[89] testthat_2.3.2 ggsignif_0.6.0 reprex_0.3.0 statmod_1.4.34 stringi_1.4.6 highr_0.8 ps_1.3.3 parameters_0.7.0
[97] desc_1.2.0 Matrix_1.2-18 nloptr_1.2.2.1 KMsurv_0.1-5 ggsci_2.9 vctrs_0.3.0 pillar_1.4.4 lifecycle_0.2.0
[105] estimability_1.3 cowplot_1.0.0 insight_0.8.4 latticeExtra_0.6-29 R6_2.4.1 gridExtra_2.3 rio_0.5.16 sessioninfo_1.1.1
[113] codetools_0.2-16 boot_1.3-25 assertthat_0.2.1 pkgload_1.0.2 rprojroot_1.3-2 withr_2.2.0 multcomp_1.4-13 parallel_3.6.3
[121] mgcv_1.8-31 bayestestR_0.6.0 hms_0.5.3 quadprog_1.5-8 rpart_4.1-15 grid_3.6.3 class_7.3-17 coda_0.19-3
[129] minqa_1.2.4 rmarkdown_2.1 carData_3.0-4 lubridate_1.7.8 base64enc_0.1-3 Saving environment
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".sample_selection.RData"))| © 1979-2020 Sander W. van der Laan | s.w.vanderlaan-2[at]umcutrecht.nl | swvanderlaan.github.io. |
---
title: "Athero-Express Biobank Study -- IL6 and MCP1 plaque levels."
author: '[Sander W. van der Laan, PhD](https://swvanderlaan.github.io) | @swvanderlaan; Marios Georgakis; Rainer Malik; Martin Dichgans'
date: '`r Sys.Date()`'
output:
  html_notebook:
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 10
    fig_retina: 2
    fig_width: 12
    theme: paper
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
mainfont: Helvetica
subtitle: An 'Athero-Express Biobank Study' project
editor_options:
  chunk_output_type: inline
---
```{r global_options, include = FALSE}
# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/',
                      eval = TRUE, warning = FALSE, message = FALSE)
```

# Preparation

Clean the environment.
```{r ClearEnvironment, include = FALSE}
rm(list = ls())
```

Set locations, and the working directory.
```{r LocalSystem, include = FALSE}
### Operating System Version
### Mac Pro
# ROOT_loc = "/Volumes/EliteProQx2Media"
# GENOMIC_loc = "/Users/svanderlaan/iCloud/Genomics"

### MacBook
ROOT_loc = "/Users/swvanderlaan"
GENOMIC_loc = paste0(ROOT_loc, "/iCloud/Genomics")

### Generic Locations
AEDB_loc = paste0(GENOMIC_loc, "/AE-AAA_GS_DBs")
LAB_loc = paste0(GENOMIC_loc, "/LabBusiness")
RESULTS = paste0(ROOT_loc, "/PLINK/analyses/lookups/AE_20190912_010_MDICHGANS_SWVDLAAN_IL6_MCP1")
RAWDATA = paste0(ROOT_loc, "/PLINK/_AE_ORIGINALS/AESCRNA/prepped_data")
PROJECT_loc = paste0(ROOT_loc, "/PLINK/analyses/lookups/AE_20190912_010_MDICHGANS_SWVDLAAN_IL6_MCP1")

### SOME VARIABLES WE NEED DOWN THE LINE
cat("\nDefining phenotypes and datasets.\n")
PROJECTNAME="IL6MCP1"
# SUBPROJECTNAME=""

cat("\nCreate a new analysis directory, including subdirectories.\n")
# Analysis
ifelse(!dir.exists(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       dir.create(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       FALSE)
ANALYSIS_loc = paste0(PROJECT_loc,"/",PROJECTNAME)

# Plots
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/PLOTS")), 
       dir.create(file.path(ANALYSIS_loc, "/PLOTS")), 
       FALSE)
PLOT_loc = paste0(ANALYSIS_loc,"/PLOTS")

# QC plots
ifelse(!dir.exists(file.path(PLOT_loc, "/QC")), 
       dir.create(file.path(PLOT_loc, "/QC")), 
       FALSE)
QC_loc = paste0(PLOT_loc,"/QC")

# Output files
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/OUTPUT")), 
       dir.create(file.path(ANALYSIS_loc, "/OUTPUT")), 
       FALSE)
OUT_loc = paste0(ANALYSIS_loc, "/OUTPUT")

# COX analysis
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/COX")), 
       dir.create(file.path(ANALYSIS_loc, "/COX")), 
       FALSE)
COX_loc = paste0(ANALYSIS_loc, "/COX")

# Baseline characteristics
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/BASELINE")), 
       dir.create(file.path(ANALYSIS_loc, "/BASELINE")), 
       FALSE)
BASELINE_loc = paste0(ANALYSIS_loc, "/BASELINE")

# Sample selection
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/SELECTIONS")), 
       dir.create(file.path(ANALYSIS_loc, "/SELECTIONS")), 
       FALSE)
SELECTIONS_loc = paste0(ANALYSIS_loc, "/SELECTIONS")

cat("\nSetting working directory and listing its contents.\n")
setwd(paste0(PROJECT_loc))
getwd()
list.files()
```

A package-installation function.
```{r Function: installations, include = FALSE}
install.packages.auto <- function(x) { 
  x <- as.character(substitute(x)) 
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else { 
    # Update installed packages - this may mean a full upgrade of R, which in turn
    # may not be warrented. 
    # update.install.packages.auto(ask = FALSE) 
    eval(parse(text = sprintf("install.packages(\"%s\", dependencies = TRUE, repos = \"https://cloud.r-project.org/\")", x)))
  }
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else {
    if (!requireNamespace("BiocManager"))
      install.packages("BiocManager")
    # BiocManager::install() # this would entail updating installed packages, which in turned may not be warrented
    eval(parse(text = sprintf("BiocManager::install(\"%s\")", x)))
    eval(parse(text = sprintf("require(\"%s\")", x)))
  }
}
```

Load those packages.
```{r Setting: loading_packages, include = FALSE}
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")

# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)

install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("MASS")
# install.packages.auto("Seurat") # latest version

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')

install.packages.auto("haven")
install.packages.auto("sjlabelled")
install.packages.auto("sjPlot")
install.packages.auto("labelled")
install.packages.auto("tableone")

install.packages.auto("ggpubr")

```

We will create a datestamp and define the Utrecht Science Park Colour Scheme.
```{r Setting: Colors, include = FALSE}

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
### 
###	No.	Color			      HEX	(RGB)						              CHR		  MAF/INFO
###---------------------------------------------------------------------------------------
###	1	  yellow			    #FBB820 (251,184,32)				      =>	1		or 1.0>INFO
###	2	  gold			      #F59D10 (245,157,16)				      =>	2		
###	3	  salmon			    #E55738 (229,87,56)				      =>	3		or 0.05<MAF<0.2 or 0.4<INFO<0.6
###	4	  darkpink		    #DB003F ((219,0,63)				      =>	4		
###	5	  lightpink		    #E35493 (227,84,147)				      =>	5		or 0.8<INFO<1.0
###	6	  pink			      #D5267B (213,38,123)				      =>	6		
###	7	  hardpink		    #CC0071 (204,0,113)				      =>	7		
###	8	  lightpurple	    #A8448A (168,68,138)				      =>	8		
###	9	  purple			    #9A3480 (154,52,128)				      =>	9		
###	10	lavendel		    #8D5B9A (141,91,154)				      =>	10		
###	11	bluepurple		  #705296 (112,82,150)				      =>	11		
###	12	purpleblue		  #686AA9 (104,106,169)			      =>	12		
###	13	lightpurpleblue	#6173AD (97,115,173/101,120,180)	=>	13		
###	14	seablue			    #4C81BF (76,129,191)				      =>	14		
###	15	skyblue			    #2F8BC9 (47,139,201)				      =>	15		
###	16	azurblue		    #1290D9 (18,144,217)				      =>	16		or 0.01<MAF<0.05 or 0.2<INFO<0.4
###	17	lightazurblue	  #1396D8 (19,150,216)				      =>	17		
###	18	greenblue		    #15A6C1 (21,166,193)				      =>	18		
###	19	seaweedgreen	  #5EB17F (94,177,127)				      =>	19		
###	20	yellowgreen		  #86B833 (134,184,51)				      =>	20		
###	21	lightmossgreen	#C5D220 (197,210,32)				      =>	21		
###	22	mossgreen		    #9FC228 (159,194,40)				      =>	22		or MAF>0.20 or 0.6<INFO<0.8
###	23	lightgreen	  	#78B113 (120,177,19)				      =>	23/X
###	24	green			      #49A01D (73,160,29)				      =>	24/Y
###	25	grey			      #595A5C (89,90,92)				        =>	25/XY	or MAF<0.01 or 0.0<INFO<0.2
###	26	lightgrey		    #A2A3A4	(162,163,164)			      =>	26/MT
###
###	ADDITIONAL COLORS
###	27	midgrey			#D7D8D7
###	28	verylightgrey	#ECECEC"
###	29	white			#FFFFFF
###	30	black			#000000
###----------------------------------------------------------------------------------------------

uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
                 "#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
                 "#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
                 "#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

#ggplot2 default color palette
gg_color_hue <- function(n) {
  hues = seq(15, 375, length = n + 1)
  hcl(h = hues, l = 65, c = 100)[1:n]
}

### ----------------------------------------------------------------------------
```


```{r Analysis Functions}
# Function to grep data from glm()/lm()
GLM.CON <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' .\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    tvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    R = summary(fit)$r.squared
    R.adj = summary(fit)$adj.r.squared
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, tvalue, pvalue, R, R.adj, AE_N, sample_size, Perc_Miss)
    
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("T-value...................:", round(tvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("R^2.......................:", round(R, 6), "\n")
    cat("Adjusted r^2..............:", round(R.adj, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}

GLM.BIN <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' ...\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,9))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data...\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    zvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    dev <- fit$deviance
    nullDev <- fit$null.deviance
    modelN <- length(fit$fitted.values)
    R.l <- 1 - dev / nullDev
    R.cs <- 1 - exp(-(nullDev - dev) / modelN)
    R.n <- R.cs / (1 - (exp(-nullDev/modelN)))
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, zvalue, pvalue, R.l, R.cs, R.n, AE_N, sample_size, Perc_Miss)
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("Z-value...................:", round(zvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("Hosmer and Lemeshow r^2...:", round(R.l, 6), "\n")
    cat("Cox and Snell r^2.........:", round(R.cs, 6), "\n")
    cat("Nagelkerke's pseudo r^2...:", round(R.n, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}
```


# Background

Using a Mendelian Randomization approach, we recently examined associations between the circulating levels of 41 cytokines and growth factors and the risk of stroke in the MEGASTROKE GWAS dataset (67,000 stroke cases and 450,000 controls) and found Monocyte chemoattractant protein-1 (MCP-1) as the cytokine showing the strongest association with stroke, particularly large artery and cardioembolic stroke (Georgakis et al., 2019a). Genetically elevated MCP-1 levels were also associated with a higher risk of coronary artery disease and myocardial infarction (Georgakis et al., 2019a). Further, in a meta-analysis of 6 observational population-based of longitudinal cohort studies we recently showed that baseline levels of MCP-1 were associated with a higher risk of ischemic stroke over follow-up (Georgakis et al., 2019b).
While these data suggest a central role of MCP-1 in the pathogenesis of atherosclerosis, it remains unknown if MCP-1 levels in the blood really reflect MCP-1 activity. MCP-1 is expressed in the atherosclerotic plaque and attracts monocytes in the subendothelial space (Nelken et al., 1991; Papadopoulou et al., 2008; Takeya et al., 1993; Wilcox et al., 1994). Thus, MCP-1 levels in the plaque might more strongly reflect MCP-1 signaling. However, it remains unknown if MCP-1 plaque levels associate with plaque vulnerability or risk of cardiovascular events.


## Objectives

Against this background we now aim to make use of the data from Athero-Express Biobank Study to explore the associations of MCP-1 protein levels in the atherosclerotic plaques from patients undergoing carotid endarterectomy with phenotypes of plaque vulnerability and secondary vascular events over a follow-up of three years.


## Methods

*Blood*

OLINK-platform 

- IL6: Interleukin 6. Entrez Gene: 3569. OLINK, plasma <!-- Bender MedSystems; cat.nr.: BMS810FF. Recalculated FACS. [pg/mL] -->
- MCP1: Monocyte chemotactic protein 1, MCP-1 (Chemokine (C-C motif) ligand 2, CCL2). Entrez Gene: 6347. OLINK, plasma <!-- Measured at the WKZ. Recalculated Luminex. [pg/mL] -->

> THESE DATA ARE NOT AVAILABLE YET

*Plaque*

Luminex-platform, measured by Luminex

- MCP1: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/ug]. Measured in two experiments, variables `MCP1` and `MCP1_pg_ug_2015`. The latter was corrected for plaque total protein concentration.
- IL6: Interleuking 6 (IL6; Entrez Gene: 3569) concentration in plaque [pg/ug].
- IL6R: Interleuking 6 receptor (IL6R; Entrez Gene: 3570) concentration in plaque [pg/ug].

FACS platform

- IL6: Interleukin 6. Entrez Gene: 3569. Bender MedSystems; cat.nr.: BMS810FF. Recalculated FACS. [pg/mL]


# Loading data

## Clinical data

Loading Athero-Express clinical data.
```{r LoadAEDB}
require(haven)

# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))
AEDBraw <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))

head(AEDBraw)
```

Loading Athero-Express plaque protein measurements from 2015.
```{r LoadAEProteins}
library(openxlsx)
AEDB_Protein_2015 <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_Proteins/Cytokines_and_chemokines_2015/20200629_MPCF015-0024.xlsx"), sheet = "for_SPSS_R")

names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "SampleID"] <- "STUDY_NUMBER"

head(AEDB_Protein_2015)

```

We will merge these measurements to the AEDB for comparing pg/ug vs. pg/mL measurements of MCP1 - also in relation to plaque phenotypes. In addition we have more information the experiment and can correct for this.

```{r merge AEDB and Proteins}
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL6_pg_ml"] <- "IL6_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL6R_pg_ml"] <- "IL6R_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL8_pg_ml"] <- "IL8_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "MCP1_pg_ml"] <- "MCP1_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "RANTES_pg_ml"] <- "RANTES_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "PAI1_pg_ml"] <- "PAI1_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "MCSF_pg_ml"] <- "MCSF_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Adiponectin_ng_ml"] <- "Adiponectin_ng_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Segment_isolated_Tris"] <- "Segment_isolated_Tris_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Tris_protein_conc_ug_ml"] <- "Tris_protein_conc_ug_ml_2015"

temp <- subset(AEDB_Protein_2015, select = c("STUDY_NUMBER", "IL6_pg_ml_2015", "IL6R_pg_ml_2015", "IL8_pg_ml_2015", "MCP1_pg_ml_2015", "RANTES_pg_ml_2015", "PAI1_pg_ml_2015", "MCSF_pg_ml_2015", "Adiponectin_ng_ml_2015", "Segment_isolated_Tris_2015", "Tris_protein_conc_ug_ml_2015"))

AEDB <- merge(AEDBraw, temp, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = FALSE,
              all.x = TRUE)
rm(temp)

temp <- subset(AEDB, select = c("STUDY_NUMBER", "MCP1", "MCP1_pg_ug_2015", "MCP1_pg_ml_2015", "Segment_isolated_Tris_2015"))

head(temp)
   
```

#### Examine AEDB

We can examine the contents of the Athero-Express Biobank dataset to know what each variable is called, what class (type) it has, and what the variable description is. 

There is an excellent post on this: https://www.r-bloggers.com/working-with-spss-labels-in-r/. 
```{r AEDB: describe}
AEDB %>% sjPlot::view_df(show.type = TRUE,
                         show.frq = TRUE,
                         show.prc = TRUE,
                         show.na = TRUE, 
                         max.len = TRUE, 
                         wrap.labels = 20,
                         verbose = FALSE, 
                         use.viewer = FALSE,
                         file = paste0(OUT_loc, "/", Today, ".AEDB.dictionary.html")) 
```


## Fixing and creating variables

We need to be very strict in defining _symptoms._ Therefore we will fix a new variable that groups _symptoms_ at inclusion.

Coding of _symptoms_ is as follows:

- missing	-999	
- Asymptomatic	0	
- TIA	1	
- minor stroke	2	
- Major stroke	3	
- Amaurosis fugax	4	
- Four vessel disease	5	
- Vertebrobasilary TIA	7	
- Retinal infarction	8	
- Symptomatic, but aspecific symtoms	9
- Contralateral symptomatic occlusion	10	
- retinal infarction	11	
- armclaudication due to occlusion subclavian artery, CEA needed for bypass	12	
- retinal infarction + TIAs	13	
- Ocular ischemic syndrome	14	
- ischemisch glaucoom	15	
- subclavian steal syndrome	16	
- TGA	17

We will group as follows in `Symptoms.5G`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13
3. Stroke > 2, 3
4. Ocular > 4, 14, 15
5. Retinal infarction > 8, 11
6. Other > 5, 9, 10, 12, 16, 17

We will also group as follows in `AsymptSympt`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13 + Stroke > 2, 3 
3. Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17

We will also group as follows in `AsymptSympt2G`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13 + Stroke > 2, 3 Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17


```{r FixSymptoms, message=FALSE, warning=FALSE}
# Fix symptoms

attach(AEDB)

AEDB$sympt[is.na(AEDB$sympt)] <- -999

# Symptoms.5G
AEDB[,"Symptoms.5G"] <- NA
# AEDB$Symptoms.5G[sympt == "NA"] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == -999] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"

# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == -999] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"

# AsymptSympt
AEDB[,"AsymptSympt2G"] <- NA
AEDB$AsymptSympt2G[sympt == -999] <- NA
AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"

detach(AEDB)

# table(AEDB$sympt, useNA = "ifany")
# table(AEDB$AsymptSympt2G, useNA = "ifany")
# table(AEDB$Symptoms.5G, useNA = "ifany")
# 
# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
# 
# rm(AEDB.temp)

```

We will also fix the _plaquephenotypes_ variable.  

Coding of symptoms is as follows:

- missing	-999	
- not relevant -888
- fibrous	1	
- fibroatheromatous	2	
- atheromatous	3	


```{r FixPlaquePhenotypes, message=FALSE, warning=FALSE}

# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)

table(AEDB$OverallPlaquePhenotype)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```

We will also fix the _diabetes_ status variable. We define diabetes as history of a diagnosis and/or use of glucose-lowering medications.

```{r FixDiabetes, message=FALSE, warning=FALSE}
# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)

table(AEDB$DM.composite)

table(AEDB$DiabetesStatus)


# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```


We will also fix the _smoking_ status variable. We are interested in whether someone never, ever or is currently (at the time of inclusion) smoking. This is based on the questionnaire. 

- `diet801`: are you a smoker?
- `diet802`: did you smoke in the past?

We already have some variables indicating smoking status:

- `SmokingReported`: patient has reported to smoke.
- `SmokingYearOR`: smoking in the year of surgery?
- `SmokerCurrent`: currently smoking?



```{r FixSmoking, message=FALSE, warning=FALSE}
require(labelled)
AEDB$diet801 <- to_factor(AEDB$diet801)
AEDB$diet802 <- to_factor(AEDB$diet802)
AEDB$diet805 <- to_factor(AEDB$diet805)
AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)

# table(AEDB$diet801)
# table(AEDB$diet802)
# table(AEDB$SmokingReported)
# table(AEDB$SmokerCurrent)
# table(AEDB$SmokingYearOR)
# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
# 
# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))

cat("\nFixing smoking status.\n")
attach(AEDB)
AEDB[,"SmokerStatus"] <- NA
AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
detach(AEDB)

cat("\n* Current smoking status.\n")
table(AEDB$SmokerCurrent,
      useNA = "ifany", 
      dnn = c("Current smoker"))

cat("\n* Updated smoking status.\n")
table(AEDB$SmokerStatus,
      useNA = "ifany", 
      dnn = c("Updated smoking status"))

cat("\n* Comparing to 'SmokerCurrent'.\n")
table(AEDB$SmokerStatus, AEDB$SmokerCurrent, 
      useNA = "ifany", 
      dnn = c("Updated smoking status", "Current smoker"))

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```

We will also fix the _alcohol_ status variable.

```{r FixAlcohol, message=FALSE, warning=FALSE}

# Fix diabetes
attach(AEDB)
AEDB[,"AlcoholUse"] <- NA
AEDB$AlcoholUse[diet810 == -999] <- NA
AEDB$AlcoholUse[diet810 == 0] <- "No"
AEDB$AlcoholUse[diet810 == 1] <- "Yes"
detach(AEDB)

table(AEDB$AlcoholUse)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```

We will also fix a history of CAD, stroke or peripheral intervention status variable. This will be based on `CAD_history`, `Stroke_history`, and `Peripheral.interv`

```{r FixCAD_History, message=FALSE, warning=FALSE}

# Fix diabetes
attach(AEDB)
AEDB[,"MedHx_CVD"] <- NA
AEDB$MedHx_CVD[CAD_history == 0 | Stroke_history == 0 | Peripheral.interv == 0] <- "No"
AEDB$MedHx_CVD[CAD_history == 1 | Stroke_history == 1 | Peripheral.interv == 1] <- "yes"
detach(AEDB)

table(AEDB$CAD_history)
table(AEDB$Stroke_history)
table(AEDB$Peripheral.interv)
table(AEDB$MedHx_CVD)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```




# Athero-Express Biobank Study

## Baseline characteristics

We are interested in the following variables at baseline.

- Age (years)
- Female sex (N, %)
- Hypertension (N, %)
- SBP (mmHg)
- DBP (mmHg)
- Diabetes mellitus (N, %)
- Total cholesterol levels (mg/dL)
- LDL cholesterol levels (mg/dL)
- HDL cholesterol levels (mg/dL)
- Triglyceride levels (mg/dL)
- Use of statins (N, %)
- Use of antiplatelet drugs (N, %)
- BMI (kg/m²)
- Smoking status (N, %)
  - Never smokers
  - Ex-smokers
  - Current smokers
- History of CAD (N, %)
- History of PAD (N, %)
- Clinical manifestations
  - Asymptomatic
  - Amaurosis fugax
  - TIA
  - Stroke
- eGFR (mL/min/1.73 m²)
- MCP-1 plaque levels (pg/mL) (LUMINEX based, two experiments `MCP1`, and `MCP1_pg_ug_2015`)

> NOT AVAILABLE YET
- MCP-1 plasma levels (pg/mL) (OLINK based) 


```{r Baseline AEDB: creation, include = FALSE}
cat("====================================================================================================\n")
cat("SELECTION THE SHIZZLE\n")

### Artery levels
# AEdata$Artery_summary: 
#           value                                                                                   label
# NOT USE - 0 No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA
# USE - 1                                                                  carotid (left & right)
# USE - 2                                               femoral/iliac (left, right or both sides)
# NOT USE - 3                                               other carotid arteries (common, external)
# NOT USE - 4                                   carotid bypass and injury (left, right or both sides)
# NOT USE - 5                                                         aneurysmata (carotid & femoral)
# NOT USE - 6                                                                                   aorta
# NOT USE - 7                                            other arteries (renal, popliteal, vertebral)
# NOT USE - 8                        femoral bypass, angioseal and injury (left, right or both sides)

### AEdata$informedconsent
#           value                                                                                           label
# NOT USE - -999                                                                                         missing
# NOT USE - 0                                                                                        no, died
# USE - 1                                                                                             yes
# USE - 2                                                             yes, health treatment when possible
# USE - 3                                                                        yes, no health treatment
# USE - 4                                                yes, no health treatment, no commercial business
# NOT USE - 5                                                          yes, no tissue, no commerical business
# NOT USE - 6                      yes, no tissue, no questionnaires, no medical info, no commercial business
# USE - 7                             yes, no questionnaires, no health treatment, no commercial business
# USE - 8                                          yes, no questionnaires, health treatment when possible
# NOT USE - 9                  yes, no tissue, no questionnaires, no health treatment, no commerical business
# USE - 10                               yes, no health treatment, no medical info, no commercial business
# NOT USE - 11 yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business
# USE - 12                                                     yes, no questionnaires, no health treatment
# NOT USE - 13                                                             yes, no tissue, no health treatment
# NOT USE - 14                                                               yes, no tissue, no questionnaires
# NOT USE - 15                                                  yes, no tissue, health treatment when possible
# NOT USE - 16                                                                                  yes, no tissue
# USE - 17                                                                     yes, no commerical business
# USE - 18                                     yes, health treatment when possible, no commercial business
# USE - 19                                                    yes, no medical info, no commercial business
# USE - 20                                                                          yes, no questionnaires
# NOT USE - 21                         yes, no tissue, no questionnaires, no health treatment, no medical info
# NOT USE - 22                  yes, no tissue, no questionnaires, no health treatment, no commercial business
# USE - 23                                                                            yes, no medical info
# USE - 24                                                  yes, no questionnaires, no commercial business
# USE - 25                                    yes, no questionnaires, no health treatment, no medical info
# USE - 26                  yes, no questionnaires, health treatment when possible, no commercial business
# USE - 27                                                      yes,  no health treatment, no medical info
# NOT USE - 28                                                                             no, doesn't want to
# NOT USE - 29                                                                              no, unable to sign
# NOT USE - 30                                                                                 no, no reaction
# NOT USE - 31                                                                                        no, lost
# NOT USE - 32                                                                                     no, too old
# NOT USE - 34                                            yes, no medical info, health treatment when possible
# NOT USE - 35                                             no (never asked for IC because there was no tissue)
# USE - 36                    yes, no medical info, no commercial business, health treatment when possible
# NOT USE - 37                                                                                    no, endpoint
# USE - 38                                                         wil niets invullen, wel alles gebruiken
# USE - 39                                           second informed concents: yes, no commercial business
# NOT USE - 40                                                                              nooit geincludeerd

cat("- sanity checking PRIOR to selection")
library(data.table)
ae.gender <- ifelse(AEDB$Gender == 0, "Female", "Male")
ae.hospital <- ifelse(AEDB$Hospital == 1, "Antonius", "UMCU")
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"))
ae.gender <- ifelse(AEDB$Gender == 0, "Female", "Male")
table(ae.gender, AEDB$Artery_summary, dnn = c("Sex", "Artery"))
# table(ae.gender, AEDB$informedconsent, dnn = c("Sex", "IC"))

rm(ae.gender, ae.hospital)

# I change numeric and factors manually because, well, I wouldn't know how to fix it otherwise
# to have this 'tibble' work with 'tableone'... :-)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$diastoli <- as.numeric(AEDB$diastoli)
AEDB$systolic <- as.numeric(AEDB$systolic)

AEDB$TC_finalCU <- as.numeric(AEDB$TC_finalCU)
AEDB$LDL_finalCU <- as.numeric(AEDB$LDL_finalCU)
AEDB$HDL_finalCU <- as.numeric(AEDB$HDL_finalCU)
AEDB$TG_finalCU <- as.numeric(AEDB$TG_finalCU)

AEDB$TC_final <- as.numeric(AEDB$TC_final)
AEDB$LDL_final <- as.numeric(AEDB$LDL_final)
AEDB$HDL_final <- as.numeric(AEDB$HDL_final)
AEDB$TG_final <- as.numeric(AEDB$TG_final)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$GFR_MDRD <- as.numeric(AEDB$GFR_MDRD)
AEDB$BMI <- as.numeric(AEDB$BMI)
AEDB$eCigarettes <- as.numeric(AEDB$eCigarettes)
AEDB$ePackYearsSmoking <- as.numeric(AEDB$ePackYearsSmoking)
AEDB$EP_composite_time <- as.numeric(AEDB$EP_composite_time)

AEDB$macmean0 <- as.numeric(AEDB$macmean0)
AEDB$smcmean0 <- as.numeric(AEDB$smcmean0)
AEDB$neutrophils <- as.numeric(AEDB$neutrophils)
AEDB$Mast_cells_plaque <- as.numeric(AEDB$Mast_cells_plaque)
AEDB$vessel_density_averaged <- as.numeric(AEDB$vessel_density_averaged)

# IL6, IL6R, MCP1 measurements
AEDB$IL6 <- as.numeric(AEDB$IL6) # FACS plaque
AEDB$IL6_pg_ug_2015 <- as.numeric(AEDB$IL6_pg_ug_2015) # LUMINEX plaque
AEDB$IL6R_pg_ug_2015 <- as.numeric(AEDB$IL6R_pg_ug_2015) # LUMINEX plaque
AEDB$MCP1 <- as.numeric(AEDB$MCP1) # LUMINEX plaque
AEDB$MCP1_pg_ug_2015 <- as.numeric(AEDB$MCP1_pg_ug_2015) # LUMINEX plaque
AEDB$MCP1_pg_ml_2015 <- as.numeric(AEDB$MCP1_pg_ml_2015) # LUMINEX plaque
AEDB$hsCRP_plasma <- as.numeric(AEDB$hsCRP_plasma) # LUMINEX

require(labelled)
AEDB$ORyear <- to_factor(AEDB$ORyear)
AEDB$Gender <- to_factor(AEDB$Gender)
AEDB$Hospital <- to_factor(AEDB$Hospital)
AEDB$KDOQI <- to_factor(AEDB$KDOQI)
AEDB$BMI_WHO <- to_factor(AEDB$BMI_WHO)
AEDB$DiabetesStatus <- to_factor(AEDB$DiabetesStatus)
AEDB$SmokerStatus <- to_factor(AEDB$SmokerStatus)
AEDB$AlcoholUse <- to_factor(AEDB$AlcoholUse)

AEDB$Hypertension.selfreport <- to_factor(AEDB$Hypertension1)
AEDB$Hypertension.selfreportdrug <- to_factor(AEDB$Hypertension2)
AEDB$Hypertension.composite <- to_factor(AEDB$Hypertension.composite)
AEDB$Hypertension.drugs <- to_factor(AEDB$Hypertension.drugs)

AEDB$Med.anticoagulants <- to_factor(AEDB$Med.anticoagulants)
AEDB$Med.all.antiplatelet <- to_factor(AEDB$Med.all.antiplatelet)
AEDB$Med.Statin.LLD <- to_factor(AEDB$Med.Statin.LLD)

AEDB$Stroke_Dx <- to_factor(AEDB$Stroke_Dx)
AEDB$CAD_history <- to_factor(AEDB$CAD_history)
AEDB$PAOD <- to_factor(AEDB$PAOD)
AEDB$Peripheral.interv <- to_factor(AEDB$Peripheral.interv)
AEDB$MedHx_CVD <- to_factor(AEDB$MedHx_CVD)


AEDB$sympt <- to_factor(AEDB$sympt)
AEDB$Symptoms.3g <- to_factor(AEDB$Symptoms.3g)
AEDB$Symptoms.4g <- to_factor(AEDB$Symptoms.4g)
AEDB$Symptoms.5G <- to_factor(AEDB$Symptoms.5G)
AEDB$AsymptSympt <- to_factor(AEDB$AsymptSympt)
AEDB$AsymptSympt2G <- to_factor(AEDB$AsymptSympt2G)


AEDB$restenos <- to_factor(AEDB$restenos)
AEDB$stenose <- to_factor(AEDB$stenose)
AEDB$EP_composite <- to_factor(AEDB$EP_composite)
AEDB$Macrophages.bin <- to_factor(AEDB$Macrophages.bin)
AEDB$SMC.bin <- to_factor(AEDB$SMC.bin)
AEDB$IPH.bin <- to_factor(AEDB$IPH.bin)
AEDB$Calc.bin <- to_factor(AEDB$Calc.bin)
AEDB$Collagen.bin <- to_factor(AEDB$Collagen.bin)
AEDB$Fat.bin_10 <- to_factor(AEDB$Fat.bin_10)
AEDB$Fat.bin_40 <- to_factor(AEDB$Fat.bin_40)
AEDB$OverallPlaquePhenotype <- to_factor(AEDB$OverallPlaquePhenotype)

AEDB$Artery_summary <- to_factor(AEDB$Artery_summary)

AEDB$informedconsent <- to_factor(AEDB$informedconsent)

AEDB.CEA <- subset(AEDB,
                    (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)") & # we only want carotids
                       informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                       informedconsent != "no, died" &
                       informedconsent != "yes, no tissue, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no health treatment" &
                       informedconsent != "yes, no tissue, no questionnaires" &
                       informedconsent != "yes, no tissue, health treatment when possible" &
                       informedconsent != "yes, no tissue" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                       informedconsent != "no, doesn't want to" &
                       informedconsent != "no, unable to sign" &
                       informedconsent != "no, no reaction" &
                       informedconsent != "no, lost" &
                       informedconsent != "no, too old" &
                       informedconsent != "yes, no medical info, health treatment when possible" &
                       informedconsent != "no (never asked for IC because there was no tissue)" &
                       informedconsent != "no, endpoint" &
                       informedconsent != "nooit geincludeerd" & 
                     !is.na(AsymptSympt2G))
# AEDB.CEA[1:10, 1:10]
dim(AEDB.CEA)
```

```{r}
cat("===========================================================================================\n")
cat("CREATE BASELINE TABLE\n")

# Baseline table variables
basetable_vars = c("Hospital", "ORyear",
                   "Age", "Gender", 
                   "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                   "restenos", "stenose",
                   "MedHx_CVD", "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time",
                   "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
                   "neutrophils", "Mast_cells_plaque",
                   "IPH.bin", "vessel_density_averaged",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
                   "IL6", "IL6_pg_ug_2015", "IL6R_pg_ug_2015",
                   "MCP1", "MCP1_pg_ug_2015", "MCP1_pg_ml_2015")

basetable_bin = c("Gender", 
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con
```

### All patients
Showing the baseline table of the whole Athero-Express Biobank.

```{r Baseline AEDB: Visualize AEDB}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
```

### CEA patients

Showing the baseline table of the CEA patients in the Athero-Express Biobank.

```{r Baseline AEDB: Visualize AEDB CEA}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB.CEA, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
```

### CEA patients with `MCP1_pg_ug_2015`

Showing the baseline table of the CEA patients in the Athero-Express Biobank with `MCP1_pg_ug_2015`.

```{r Baseline AEDB: Visualize subsetCEA}
AEDB.CEA.subset <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015))

AEDB.CEA.subset.AsymptSympt.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:6]
```

### CEA patients with `MCP1_pg_ug_2015` and `MCP1`

Showing the baseline table of the CEA patients in the Athero-Express Biobank with `MCP1_pg_ug_2015` _and_ `MCP1`.

```{r Baseline AEDB: Visualize subsetCEA with MCP1}

AEDB.CEA.subset.combo <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) | !is.na(MCP1))

AEDB.CEA.subset.combo.tableOne = print(CreateTableOne(vars = basetable_vars,
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.combo, includeNA = TRUE),
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
                          format = "pf",
                          contDigits = 3)[,1:6]
```

### CEA patients with plasma MCP1 levels

Showing the baseline table of the CEA patients in the Athero-Express Biobank with plasma MCP1 levels.

> NOT AVAILABLE YET

```{r Baseline AEDB: Visualize subsetCEA with plasma MCP1}
AEDB.CEA.subset.plasma <- subset(AEDB.CEA, !is.na(MCP1_plasma))

AEDB.CEA.subset.plasma.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.plasma, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:6]
```


### CEA patients with plasma _and_ plaque MCP1 levels

Showing the baseline table of the CEA patients in the Athero-Express Biobank with both plasma and plaque MCP1 levels.

> NOT AVAILABLE YET

```{r Baseline AEDB: Visualize subsetCEA with plasma MCP1 and plaque MCP1}
AEDB.CEA.subset.both <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) & !is.na(MCP1))

AEDB.CEA.subset.both.tableOne = print(CreateTableOne(vars = basetable_vars,
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.both, includeNA = TRUE),
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
                          format = "pf",
                          contDigits = 3)[,1:6]
```

Writing the baseline table to Excel format. 
```{r Baseline AEDB: write}
# Write basetable
require(openxlsx)

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.xlsx"),
           AEDB.CEA.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.AsymptSympt.xlsx"),
           AEDB.CEA.subset.AsymptSympt.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline_Sympt")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEA.xlsx"),
           AEDB.CEA.subset.combo.tableOne,
           row.names = TRUE,
           col.names = TRUE,
           sheetName = "subsetAEDB_Baseline")

# write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEAplasma.AsymptSympt.xlsx"),
#            AEDB.CEA.subset.plasma.tableOne, 
#            row.names = TRUE, 
#            col.names = TRUE, 
#            sheetName = "subsetAEDB_Baseline_plasma_Sympt")

```


## Data exploration

Here we inspect the data and when necessary transform quantitative measures. We will inspect the raw, natural log transformed + the smallest measurement, and inverse-normal transformation. 

### MCP1 plaque levels: experiment 2

We will explore the plaque levels. As noted above, we will use `MCP1_pg_ug_2015`, this was experiment 2 in 2015 on the LUMINEX-platform and measurements were corrected for total plaque protein content.

```{r DataExploration: MCP1 plaque Exp2}

summary(AEDB.CEA$MCP1_pg_ug_2015)

do.call(rbind , by(AEDB.CEA$MCP1_pg_ug_2015, AEDB.CEA$AsymptSympt2G, summary))


summary(AEDB.CEA$MCP1_pg_ml_2015)

do.call(rbind , by(AEDB.CEA$MCP1_pg_ml_2015, AEDB.CEA$AsymptSympt2G, summary))
```

```{r DataExploration: MCP1 plaque Exp2 visual}
library(patchwork)
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "pg/ug", 
                    ggtheme = theme_minimal())

min_MCP1_pg_ug_2015 <- min(AEDB.CEA$MCP1_pg_ug_2015, na.rm = TRUE)
min_MCP1_pg_ug_2015

AEDB.CEA$MCP1_pg_ug_2015_LN <- log(AEDB.CEA$MCP1_pg_ug_2015 + min_MCP1_pg_ug_2015)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    # title = "MCP1 plaque levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$MCP1_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ug_2015)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 plaque levels",
                    xlab = "inverse-normal transformation pg/ug",
                    ggtheme = theme_minimal())

p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p2, p3)
```

We will explore the `MCP1_pg_ml_2015` levels and compare to the protein content corrected ones. 

```{r DataExploration: MCP1 plaque pg-ml Exp2 visual}
library(patchwork)
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ml_2015", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "pg/mL", 
                    ggtheme = theme_minimal())

min_MCP1_pg_ml_2015 <- min(AEDB.CEA$MCP1_pg_ml_2015, na.rm = TRUE)
min_MCP1_pg_ml_2015

AEDB.CEA$MCP1_pg_ml_2015_LN <- log(AEDB.CEA$MCP1_pg_ml_2015 + min_MCP1_pg_ml_2015)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ml_2015_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    # title = "MCP1 plaque levels",
                    xlab = "natural log-transformed pg/mL", 
                    ggtheme = theme_minimal())

AEDB.CEA$MCP1_pg_ml_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ml_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ml_2015)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ml_2015_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 plaque levels",
                    xlab = "inverse-normal transformation pg/mL",
                    ggtheme = theme_minimal())

p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p2, p3)
```

### MCP1 plaque levels: experiment 1

We will explore the plaque levels. As noted above, we will use `MCP1`, this was experiment 1 on the LUMINEX-platform and measurements were corrected for total plaque protein content.

```{r DataExploration: MCP1 plaque Exp1}

# summary(AEDB.CEA$MCP1)
# 
# do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary))
# 
# attach(AEDB.CEA)
AEDB.CEA$MCP1[MCP1 == 0] <- NA
# detach(AEDB.CEA)

summary(AEDB.CEA$MCP1)

do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary))

```

```{r DataExploration: MCP1 plaque Exp1 visual}
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "pg/mL", 
                    ggtheme = theme_minimal())

min_MCP1 <- min(AEDB.CEA$MCP1, na.rm = TRUE)
min_MCP1

AEDB.CEA$MCP1_LN <- log(AEDB.CEA$MCP1 + min_MCP1)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 plaque levels",
                    xlab = "inverse-normal transformation pg/ug",
                    ggtheme = theme_minimal())

p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p2, p3)

```

#### Correlations between MCP1 plaque levels and transformations

Here we compare the MCP1 plaque levels from experiment 1 with those experiment 2. The latter we measured in pg/mL and also corrected for the total protein content (pg/ug).
```{r MCP1 scatters: transformations}
p1 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "MCP1_rank", 
                        y = "MCP1_pg_ml_2015_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "experiment 1",
                        ylab = "experiment 2",
                        title = "MCP1 plaque levels, INT, [pg/mL]",
                        ggtheme = theme_minimal())

p1 

p2 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "MCP1_rank", 
                        y = "MCP1_pg_ug_2015_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "experiment 1",
                        ylab = "experiment 2",
                        title = "MCP1 plaque levels, INT, [pg/mL]/[pg/ug]",
                        ggtheme = theme_minimal())

p2 

p3 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "MCP1_pg_ml_2015_rank", 
                        y = "MCP1_pg_ug_2015_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "experiment 2, [pg/mL]",
                        ylab = "experiment 2, [pg/ug]",
                        title = "MCP1 plaque levels, INT",
                        ggtheme = theme_minimal())

p3

```



### MCP1 plasma levels 

> NOT AVAILABLE YET


## Preliminary conclusion data exploration

In line with the previous work by [Marios Georgakis](https://www.ahajournals.org/doi/full/10.1161/CIRCRESAHA.119.315380){target="_blank"} we will apply _natural log transformation_ on all proteins and focus the analysis on MCP1 in plasma and plaque.

# Analyses

The analyses are focused on three elements: 

1) plaque vulnerability phenotypes
2) clinical status at inclusion (symptoms)
3) secondary clinical outcome during three (3) years of follow-up

## Covariates & other variables

1.  Age (continuous in 1-year increment). [`Age`]
2.  Sex (male vs. female). [`Gender`]
3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [`Hypertension.composite`]
4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes and/or administration of glucose lowering medication). [`DiabetesStatus`]
5.  Smoking (current, ex-, never). [`SmokerStatus`]
6.  LDL-C levels (continuous). [`LDL_final`]
7.  Use of lipid-lowering drugs. [`Med.Statin.LLD`]
8.  Use of antiplatelet drugs. [`Med.all.antiplatelet`]
9.  eGFR (continuous). [`GFR_MDRD`]
10.	BMI (continuous). [`BMI`]
11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [`MedHx_CVD`] combination of [`CAD_history`, `Stroke_history`, `Peripheral.interv`]
12.	Level of stenosis (50-70% vs. 70-99%). [`stenose`]
13. Year of surgery [`ORdate_year`] as we discovered in Van Lammeren _et al._ the composition of the plaque and therefore the Athero-Express Biobank Study has changed over the years. Likely through changes in lifestyle and primary prevention regimes.

## Models

We will analyze the data through four different models

- Model 1: adjusted for age, sex, and year of surgery
- Model 2: adjusted for age, sex, year of surgery, and additionally adjusted for history hypertension (defined from medical history and/or use of antihypertensive medications), diabetes (defined as history of a diagnosis and/or use of glucose-lowering medications), current smoking, LDL-C levels at time of operation, use of statins, use of antiplatelet agents, eGFR, BMI, history of cardiovascular disease (coronary artery disease, stroke, peripheral artery disease), and level of stenosis (50-70%, 70-90%, 90-99%)

## A. Cross-sectional analysis plaque phenotypes

In the cross-sectional analysis of plaque and plasma MCP1, IL6, and IL6R levels we will focus on the following plaque vulnerability phenotypes:

- Percentage of macrophages (continuous trait)
- Percentage of SMCs (continuous trait)
- Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
- Presence of moderate/heavy calcifications (binary trait)
- Presence of moderate/heavy collagen content (binary trait)
- Presence of lipid core no/<10% vs. >10% (binary trait)
- Presence of intraplaque hemorrhage (binary trait)

*Continous traits*
```{r CrossSec: plaques - transformations and visualisations continuous}

# macrophages
cat("Summary of data.\n")
summary(AEDB.CEA$macmean0)

min_macmean <- min(AEDB.CEA$macmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))

AEDB.CEA$Macrophages_LN <- log(AEDB.CEA$macmean0 + min_macmean)

ggpubr::gghistogram(AEDB.CEA, "Macrophages_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())

AEDB.CEA$Macrophages_rank <- qnorm((rank(AEDB.CEA$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$macmean0)))
ggpubr::gghistogram(AEDB.CEA, "Macrophages_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# smooth muscle cells
cat("Summary of data.\n")
summary(AEDB.CEA$macmean0)

min_smcmean <- min(AEDB.CEA$smcmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))

AEDB.CEA$SMC_LN <- log(AEDB.CEA$smcmean0 + min_smcmean)

ggpubr::gghistogram(AEDB.CEA, "SMC_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())

AEDB.CEA$SMC_rank <- qnorm((rank(AEDB.CEA$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$smcmean0)))
ggpubr::gghistogram(AEDB.CEA, "SMC_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# vessel density
cat("Summary of data.\n")
summary(AEDB.CEA$vessel_density_averaged)

min_vesseldensity <- min(AEDB.CEA$vessel_density_averaged, na.rm = TRUE)
min_vesseldensity
cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))

AEDB.CEA$VesselDensity_LN <- log(AEDB.CEA$vessel_density_averaged + min_vesseldensity)

ggpubr::gghistogram(AEDB.CEA, "VesselDensity_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                    xlab = "natural log-transformed number", 
                    ggtheme = theme_minimal())

AEDB.CEA$VesselDensity_rank <- qnorm((rank(AEDB.CEA$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$vessel_density_averaged)))
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                   xlab = "inverse-rank normalized number", 
                    ggtheme = theme_minimal())
```

*Binary traits*
```{r CrossSec: plaques - transformations and visualisations binary}

# calcification
cat("Summary of data.\n")
summary(AEDB.CEA$Calc.bin)
contrasts(AEDB.CEA$Calc.bin)

AEDB.CEA$CalcificationPlaque <- as.factor(AEDB.CEA$Calc.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CalcificationPlaque)) %>%
  group_by(Gender, CalcificationPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Calcification",
                    xlab = "calcification", 
                    ggtheme = theme_minimal())
rm(df)

# collagen
cat("Summary of data.\n")
summary(AEDB.CEA$Collagen.bin)
contrasts(AEDB.CEA$Collagen.bin)

AEDB.CEA$CollagenPlaque <- as.factor(AEDB.CEA$Collagen.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CollagenPlaque)) %>%
  group_by(Gender, CollagenPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Collagen",
                    xlab = "collagen", 
                    ggtheme = theme_minimal())
rm(df)

# fat 10%
cat("Summary of data.\n")
summary(AEDB.CEA$Fat.bin_10)
contrasts(AEDB.CEA$Fat.bin_10)

AEDB.CEA$Fat10Perc <- as.factor(AEDB.CEA$Fat.bin_10)

df <- AEDB.CEA %>%
  filter(!is.na(Fat10Perc)) %>%
  group_by(Gender, Fat10Perc) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque fat",
                    xlab = "intraplaque fat", 
                    ggtheme = theme_minimal())
rm(df)

# macrophages binned
cat("Summary of data.\n")
summary(AEDB.CEA$Macrophages.bin)
contrasts(AEDB.CEA$Macrophages.bin)

AEDB.CEA$MAC_binned <- as.factor(AEDB.CEA$Macrophages.bin)

df <- AEDB.CEA %>%
  filter(!is.na(MAC_binned)) %>%
  group_by(Gender, MAC_binned) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "MAC_binned", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Macrophages (binned)",
                    xlab = "Macrophages", 
                    ggtheme = theme_minimal())
rm(df)

# macrophages grouped
cat("Summary of data.\n")
AEDB.CEA$macrophages <- as.factor(AEDB.CEA$macrophages)
summary(AEDB.CEA$macrophages)
contrasts(AEDB.CEA$macrophages)

AEDB.CEA$MAC_grouped <- as.factor(AEDB.CEA$macrophages)

df <- AEDB.CEA %>%
  filter(!is.na(MAC_grouped)) %>%
  group_by(Gender, MAC_grouped) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "MAC_grouped", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Macrophages (grouped)",
                    xlab = "Macrophages", 
                    ggtheme = theme_minimal())
rm(df)

# SMC binned
cat("Summary of data.\n")
summary(AEDB.CEA$SMC.bin)
contrasts(AEDB.CEA$SMC.bin)

AEDB.CEA$SMC_binned <- as.factor(AEDB.CEA$SMC.bin)

df <- AEDB.CEA %>%
  filter(!is.na(SMC_binned)) %>%
  group_by(Gender, SMC_binned) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "SMC_binned", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "SMC (binned)",
                    xlab = "SMC", 
                    ggtheme = theme_minimal())
rm(df)

# SMC grouped
cat("Summary of data.\n")
AEDB.CEA$smc <- as.factor(AEDB.CEA$smc)
summary(AEDB.CEA$smc)
contrasts(AEDB.CEA$smc)

AEDB.CEA$SMC_grouped <- as.factor(AEDB.CEA$smc)

df <- AEDB.CEA %>%
  filter(!is.na(SMC_grouped)) %>%
  group_by(Gender, SMC_grouped) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "SMC_grouped", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "SMC (grouped)",
                    xlab = "SMC", 
                    ggtheme = theme_minimal())
rm(df)


# IPH
cat("Summary of data.\n")
summary(AEDB.CEA$IPH.bin)
contrasts(AEDB.CEA$IPH.bin)

AEDB.CEA$IPH <- as.factor(AEDB.CEA$IPH.bin)

df <- AEDB.CEA %>%
  filter(!is.na(IPH)) %>%
  group_by(Gender, IPH) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "IPH", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque hemorrhage",
                    xlab = "intraplaque hemorrhage", 
                    ggtheme = theme_minimal())
rm(df)

# Symptoms
cat("Summary of data.\n")
summary(AEDB.CEA$AsymptSympt)
contrasts(AEDB.CEA$AsymptSympt)

AEDB.CEA$AsymptSympt <- as.factor(AEDB.CEA$AsymptSympt)

df <- AEDB.CEA %>%
  filter(!is.na(AsymptSympt)) %>%
  group_by(Gender, AsymptSympt) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Symptoms",
                    xlab = "symptoms", 
                    ggtheme = theme_minimal())
rm(df)

```

#### Correlations between MCP1 plaque levels and surgery year

Here we compare the MCP1 plaque levels from experiment 1 with those experiment 2. The latter we measured in pg/mL and also corrected for the total protein content (pg/ug).

```{r MCP1 scatters: year of surgery}
p1 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "ORyear", 
                        y = "MCP1_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "year of surgery",
                        ylab = "experiment 1",
                        title = "MCP1 plaque levels, INT, [pg/mL]",
                        ggtheme = theme_minimal())

p1 

p2 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "ORyear", 
                        y = "MCP1_pg_ml_2015_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "year of surgery",
                        ylab = "experiment 2, [pg/mL]",
                        title = "MCP1 plaque levels, INT, [pg/mL]/[pg/ug]",
                        ggtheme = theme_minimal())

p2 

p3 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "ORyear", 
                        y = "MCP1_pg_ug_2015_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "year of surgery",
                        ylab = "experiment 2, [pg/ug]",
                        title = "MCP1 plaque levels, INT",
                        ggtheme = theme_minimal())

p3

```
In this section we make some variables to assist with analysis.
```{r CrossSec: plaques - setup regression }
AEDB.CEA.samplesize = nrow(AEDB.CEA)
# TRAITS.PROTEIN = c("IL6_LN", "MCP1_LN", "IL6_pg_ug_2015_LN", "IL6R_pg_ug_2015_LN", "MCP1_pg_ug_2015_LN")
# TRAITS.PROTEIN.RANK = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank")
# TRAITS.PROTEIN.RANK = c("MCP1_pg_ug_2015_rank", "MCP1_rank")
TRAITS.PROTEIN.RANK = c("MCP1_pg_ug_2015_rank", "MCP1_pg_ml_2015_rank", "MCP1_rank")

# TRAITS.CON = c("Macrophages_LN", "SMC_LN", "VesselDensity_LN")
# TRAITS.CON.RANK = c("Macrophages_rank")
TRAITS.CON.RANK = c("Macrophages_rank", "SMC_rank", "VesselDensity_rank")

# TRAITS.BIN = c("MAC_binned")
TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH",
               "MAC_binned", "SMC_binned")


# "Hospital", 
# "Age", "Gender", 
# "TC_final", "LDL_final", "HDL_final", "TG_final", 
# "systolic", "diastoli", "GFR_MDRD", "BMI", 
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite", 
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin", 
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015", 
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"

# 1.  Age (continuous in 1-year increment). [Age]
# 2.  Sex (male vs. female). [Gender]
# 3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5.  Smoking (current, ex-, never). [SmokerCurrent]
# 6.  LDL-C levels (continuous). [LDL_final]
# 7.  Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8.  Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9.  eGFR (continuous). [GFR_MDRD]
# 10.	BMI (continuous). [BMI]
# 11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combinatino of: [CAD_history, Stroke_history, Peripheral.interv]
# 12.	Level of stenosis (50-70% vs. 70-99%). [stenose]

# Models 
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,

AEDB.CEA$ORdate_epoch <- as.numeric(AEDB.CEA$dateok)
AEDB.CEA$ORdate_year <- as_factor(year(AEDB.CEA$dateok))
COVARIATES_M1 = c("Age", "Gender", "ORdate_year")
# COVARIATES_M1 = c("Age", "Gender", "ORdate_epoch")

COVARIATES_M2 = c(COVARIATES_M1,  
               "Hypertension.composite", "DiabetesStatus", 
               "SmokerStatus", 
               # "SmokerCurrent",
               "Med.Statin.LLD", "Med.all.antiplatelet", 
               "GFR_MDRD", "BMI", 
               # "CAD_history", "Stroke_history", "Peripheral.interv", 
               "MedHx_CVD",
               "stenose")

# COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")

# COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")

# COVARIATES_M5 = c(COVARIATES_M2, "IL6_pg_ug_2015_LN")
# COVARIATES_M5rank = c(COVARIATES_M2, "IL6_pg_ug_2015_rank")

```

### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

#### Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of plasma/plaque MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

#### Binary plaque traits

Analysis of binary plaque traits as a function of plasma/plaque MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year,
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


### Model 2

In this model we correct for _Age_, _Gender_, _year of surgery_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

#### Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of plasma/plaque MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

#### Binary plaque traits

Analysis of binary plaque traits as a function of plasma/plaque MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## B. Cross-sectional analysis symptoms

We will perform a cross-sectional analysis between plaque and plasma MCP1, IL6, and IL6R levels and the 'clinical status' of the plaque in terms of presence of patients' symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:

- stroke
- TIA
- retinal infarction
- amaurosis fugax
- asymptomatic

### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.


```{r CrossSec: symptoms - logistic regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis._.


```{r CrossSec: symptoms - logistic regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + 
                 Hypertension.composite + DiabetesStatus + SmokerStatus + 
                 Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                 MedHx_CVD + stenose, 
               data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## C. Longitudinal analysis secondary clinical outcome

For the longitudinal analyses of plaque and plasma MCP1, IL6, and IL6R levels and secondary cardiovascular events over a three-year follow-up period. 

The _primary outcome_ is defined as "a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death", i.e. major adverse cardiovascular events (MACE). Variable: `epmajor.3years`, these include:
- myocardial infarction (MI)
- cerebral infarction (CVA/stroke)
- cardiovascular death (exact cause to be investigated)
- cerebral bleeding (CVA/stroke)
- fatal myocardial infarction (MI)
- fatal cerebral infarction
- fatal cerebral bleeding
- sudden death
- fatal heart failure
- fatal aneurysm rupture
- other cardiovascular death..

The _secondary outcomes_ will be 

- incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable: `epstroke.3years`, these include:
  - cerebral infarction (CVA/stroke)
  - cerebral bleeding (CVA/stroke)
  - fatal cerebral infarction
  - fatal cerebral bleeding.
- incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable: `epcoronary.3years`, these include:
  - myocardial infarction (MI)
  - coronary angioplasty (PCI/PTCA)
  - cardiovascular death (exact cause to be investigated)
  - coronary bypass (CABG)
  - fatal myocardial infarction (MI)
  - sudden death.
- cardiovascular death - variable: `epcvdeath.3years`, these include:
  - cardiovascular death (exact cause to be investigated)
  - fatal myocardial infarction (MI)
  - fatal cerebral infarction
  - fatal cerebral bleeding
  - sudden death
  - fatal heart failure
  - fatal aneurysm rupture
  - other cardiovascular death..

### 30- and 90-days FU events

We will use 3-year follow-up, but we will also calculate 30 days and 90 days follow-up 'time-to-event' variables. On average there are 365.25 days in a year. We can calculate 30-days and 90-days follow-up time based on the three years follow-up. 

```{r Calculate new FU cut-offs: maximum FU}
cutt.off.30days = (1/365.25) * 30
cutt.off.90days = (1/365.25) * 90

# Fix maximum FU of 30 and 90 days
AEDB <- AEDB %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

AEDB.CEA <- AEDB.CEA %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)

DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)

rm(AEDB.CEA.temp)


```

Here we will calculate the new 30- and 90-days follow-up of the events and their event-times of interest:

- MACE (`epmajor.3years`)
- Stroke (`epstroke.3years`)
- Coronary events (`epcoronary.3years`)
- Cardiovascular death (`epcvdeath.3years`)


```{r Calculate new FU cut-offs: times}
avg_days_in_year = 365.25
cutt.off.30days.scaled <- cutt.off.30days * 365.25
cutt.off.90days.scaled <- cutt.off.90days * 365.25
# Event times
AEDB <- AEDB %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

AEDB.CEA <- AEDB.CEA %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

```


```{r Cox-regressions: new times}

attach(AEDB)
AEDB[,"epmajor.30days"] <- AEDB$epmajor.3years
AEDB$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AEDB[,"epstroke.30days"] <- AEDB$epstroke.3years
AEDB$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AEDB[,"epcoronary.30days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AEDB[,"epcvdeath.30days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AEDB[,"epmajor.90days"] <- AEDB$epmajor.3years
AEDB$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AEDB[,"epstroke.90days"] <- AEDB$epstroke.3years
AEDB$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AEDB[,"epcoronary.90days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AEDB[,"epcvdeath.90days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AEDB)

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

attach(AEDB.CEA)
AEDB.CEA[,"epmajor.30days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epstroke.30days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epcoronary.30days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epcvdeath.30days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epmajor.90days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epstroke.90days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epcoronary.90days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epcvdeath.90days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AEDB.CEA)

AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)

DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)

rm(AEDB.CEA.temp)



```

### Sanity checks

First we do some sanity checks and inventory the time-to-event and event variables.
```{r Cox-regressions: General}
# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")
install.packages.auto("survminer")
install.packages.auto("Hmisc")

cat("* Creating function to summarize Cox regression and prepare container for results.")
# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, protein){
  cat("Summarizing Cox regression results for '", protein ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
  if (nrow(summary(coxfit)$coefficients) == 1) {
    output = c(protein, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    cox.sum <- summary(coxfit)
    cox.effectsize = cox.sum$coefficients[1,1]
    cox.SE = cox.sum$coefficients[1,3]
    cox.HReffect = cox.sum$coefficients[1,2]
    cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
    cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
    cox.zvalue = cox.sum$coefficients[1,4]
    cox.pvalue = cox.sum$coefficients[1,5]
    cox.sample_size = cox.sum$n
    cox.nevents = cox.sum$nevent
    
    output = c(DATASET, OUTCOME, protein, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
    cat("We have collected the following:\n")
    cat("Dataset used..............:", DATASET, "\n")
    cat("Outcome analyzed..........:", OUTCOME, "\n")
    cat("Protein...................:", protein, "\n")
    cat("Effect size...............:", round(cox.effectsize, 6), "\n")
    cat("Standard error............:", round(cox.SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
    cat("T-value...................:", round(cox.zvalue, 6), "\n")
    cat("P-value...................:", signif(cox.pvalue, 8), "\n")
    cat("Sample size in model......:", cox.sample_size, "\n")
    cat("Number of events..........:", cox.nevents, "\n")
  }
  return(output)
  print(output)
} 

times = c("ep_major_t_3years", 
          "ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")

endpoints = c("epmajor.3years", 
              "epstroke.3years", "epcoronary.3years", "epcvdeath.3years")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints){
  require(labelled)
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}

times30 = c("ep_major_t_30days", 
          "ep_stroke_t_30days", "ep_coronary_t_30days", "ep_cvdeath_t_30days")

endpoints30 = c("epmajor.30days", 
              "epstroke.30days", "epcoronary.30days", "epcvdeath.30days")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints30){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times30){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times30){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer30Days.",eventtime,".pdf"), plot = last_plot())
}

times90 = c("ep_major_t_90days", 
          "ep_stroke_t_90days", "ep_coronary_t_90days", "ep_cvdeath_t_90days")

endpoints90 = c("epmajor.90days", 
              "epstroke.90days", "epcoronary.90days", "epcvdeath.90days")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints90){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times90){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times90){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer90Days.",eventtime,".pdf"), plot = last_plot())
}


```


### Cox regressions

Let's perform the actual Cox-regressions. We will apply a couple of models: 

- Model 1: adjusted for age, sex, and year of surgery
- Model 2: adjusted for age, sex, year of surgery, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis

#### 3 years follow-up

*MODEL 1*
```{r Cox-regression Analysis: Simple model}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

*MODEL 2*
```{r Cox-regression Analysis: MODEL 2}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```


#### 30-days follow-up

*MODEL 1*
```{r Cox-regression Analysis: Simple model, 30 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.30days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

*MODEL 2*
```{r Cox-regression Analysis: MODEL 2, 30 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.30days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```


#### 90-days follow-up

*MODEL 1*
```{r Cox-regression Analysis: Simple model, 90 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.90days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

*MODEL 2*
```{r Cox-regression Analysis: MODEL 2, 90 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.90days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```



# Correlations

## All biomarkers
We correlated plasma and plaque levels of the biomarkers.

```{r CrossSampleType Correlations}

# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)


# Creating matrix - inverse-rank transformation
# --------------------------------
# AEDB.CEA.temp <- subset(AEDB.CEA, 
#                           select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
# AEDB.CEA.temp <- subset(AEDB.CEA, 
#                           select = c("MCP1_rank", "MCP1_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_pg_ug_2015_rank",
                                               TRAITS.BIN, TRAITS.CON.RANK)
                                    )


AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$MAC_binned <- as.numeric(AEDB.CEA.temp$MAC_binned)
AEDB.CEA.temp$SMC_binned <- as.numeric(AEDB.CEA.temp$SMC_binned)
str(AEDB.CEA.temp)
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))


# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}

corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)

# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}


chart.Correlation.new(AEDB.CEA.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)


# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:

# ggpairs(AEDB.CEA, 
#         columns = c("MCP1_rank", "MCP1_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK), 
#         columnLabels = c("MCP1 (plasma)", "MCP1", 
#                          "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density"),
#         method = c("spearman"),
#         # ggplot2::aes(colour = Gender),
#         progress = FALSE) 

ggpairs(AEDB.CEA,
        columns = c("MCP1_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK),
        columnLabels = c("MCP1",
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages (binned)", "SMC (binned)", "Macrophages", "SMC", "Vessel density"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE)

```

## Circulating MCP1

Finally, we explored in a sub-sample, where circulating MCP-1 levels are available, the following:

1. A correlation between MCP-1 levels in the plaque and circulating MCP-1 levels
2. Associations of circulating MCP-1 levels with plaque vulnerability characteristics
3. Associations of circulating MCP-1 levels with the status of the plaque in terms of presence of symptoms (symptomatic vs. asymptomatic)
4. Associations of circulating MCP-1 levels with the primary composite endpoint of secondary cardiovascular events.

> NOT AVAILABLE YET

```{r MCP1 plasma Correlations}

# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)


# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_rank", 
                                     TRAITS.BIN, TRAITS.CON.RANK, 
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )

AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.plasma.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers_plasma.rank <- round(cor(AEDB.CEA.matrix.plasma.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_plasma_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.plasma.RANK, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers_plasma.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_plasma_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))


# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}


corr_biomarkers_plasma.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers_plasma.rank, corr_biomarkers_plasma_p.rank))
DT::datatable(corr_biomarkers_plasma.rank.df)

# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}

chart.Correlation.new(AEDB.CEA.matrix.plasma.RANK, method = "spearman", histogram = TRUE, pch = 3)


# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:
ggpairs(AEDB.CEA,
        columns = c("MCP1_rank", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"), 
        columnLabels = c("MCP1 (plasma)", 
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density",
                         "Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE) 
```

# Additional figures

## Age and sex
We want to create per-age-group figures. 

- Box and Whisker plot for MCP-1 plaque levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
- Box and Whisker plot for MCP-1 plasma levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
- Scatter plot of the correlation and regression line between MCP-1 levels in plaque (y axis) and plasma (x axis).


```{r AgeGroups}
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroup = factor(case_when(Age < 55 ~ "<55",
                                                     Age >= 55  & Age <= 64 ~ "55-64",
                                                     Age >= 65  & Age <= 74 ~ "65-74",
                                                     Age >= 75  & Age <= 84 ~ "75-84",
                                                     Age >= 85 ~ "85+"))) 

AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroupSex = factor(case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
                                                        Age >= 55  & Age <= 64 & Gender == "male"~ "55-64 males",
                                                        Age >= 65  & Age <= 74 & Gender == "male"~ "65-74 males",
                                                        Age >= 75  & Age <= 84 & Gender == "male"~ "75-84 males",
                                                        Age >= 85 & Gender == "male"~ "85+ males",
                                                        Age < 55 & Gender == "female" ~ "<55 females" ,
                                                        Age >= 55  & Age <= 64 & Gender == "female"~ "55-64 females ",
                                                        Age >= 65  & Age <= 74 & Gender == "female"~ "65-74 females",
                                                        Age >= 75  & Age <= 84 & Gender == "female"~ "75-84 females",
                                                        Age >= 85 & Gender == "female"~ "85+ females")))

table(AEDB.CEA$AgeGroup, AEDB.CEA$Gender)
table(AEDB.CEA$AgeGroupSex)

```

Now we can draw some graphs of plasma/plaque MCP1 levels per sex and age group.

### Inverse-rank transformed data

#### MCP1 plaque levels

```{r MCP1 per AgeGroup per Sex, ranked}

# ?ggpubr::ggboxplot()

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
```

#### MCP1 plasma levels

> NOT AVAILABLE YET

```{r MCP1 per AgeGroup per Sex, ranked plasma}
# compare_means(MCP1_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("Gender"),
                  y = "MCP1_rank",
                  xlab = "Gender",
                  ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("AgeGroup"),
                  y = "MCP1_rank",
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")

```

### Raw data

Simalarly but now for the raw data as median ± interquartile range.

#### MCP1 plaque levels


```{r MCP1 per AgeGroup per Sex}

# ?ggpubr::ggboxplot()
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
```

### MCP1 plasma levels

> NOT AVAILABLE YET

```{r MCP1 per AgeGroup per Sex, plasma}
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1", 
                  xlab = "Gender",
                  ylab = "MCP1 plasma [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plasma [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
```


## Hypertension & blood pressure
We want to create figures of MCP1 levels stratified by hypertension/blood pressure, and use of anti-hypertensive drugs. 

- Box and Whisker plot for MCP-1 plaque levels by hypertension group (no, yes)
- Box and Whisker plot for MCP-1 plasma levels by systolic blood pressure group (<120, 120-139, 140-159,160+)


```{r BloodPressure}
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(SBPGroup = factor(case_when(systolic < 120 ~ "<120",
                                                     systolic >= 120  & systolic <= 139 ~ "120-139",
                                                     systolic >= 140  & systolic <= 159 ~ "140-159",
                                                     systolic >= 160 ~ "160+"))) 

table(AEDB.CEA$SBPGroup, AEDB.CEA$Gender)

```

Now we can draw some graphs of plasma/plaque MCP1 levels per sex and hypertension/blood pressure group.

### Inverse-rank transformed data

#### MCP1 plaque levels

```{r MCP1 per BloodPressure per Sex, ranked}

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Systolic blood pressure (mmHg) per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Self-reported hypertension per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), 
                  x = c("Hypertension.selfreport"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Self-reported hypertension per medication use",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")



```

#### MCP1 plasma levels

> NOT AVAILABLE YET

```{r MCP1 per BloodPressure per Sex, ranked plasma}

ggpubr::ggboxplot(AEDB.CEA,
                  x = c("SBPGroup"),
                  y = "MCP1_rank",
                  xlab = "Systolic blood pressure (mmHg) per gender",
                  ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") 

```

### Raw data

Simalarly but now for the raw data as median ± interquartile range.

#### MCP1 plaque levels


```{r MCP1 per BloodPressure per Sex}

ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Systolic blood pressure (mmHg) per gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))

ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Self-reported hypertension per gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))

ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), 
                  x = c("Hypertension.selfreport"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Self-reported hypertension per medication use",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") 
```

### MCP1 plasma levels

> NOT AVAILABLE YET

```{r MCP1 per BloodPressure per Sex, plasma}

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("SBPGroup"),
                  y = "MCP1", 
                  xlab = "Systolic blood pressure (mmHg) per gender",
                  ylab = "MCP1 plasma [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
```


## Hypercholesterolemia & LDL levels
We want to create figures of MCP1 levels stratified by hypercholesterolemia/LDL-levels, and use of lipid-lowering drugs. 

- Box and Whisker plot for MCP-1 plaque levels by hypercholesterolemia group (no, yes)
- Box and Whisker plot for MCP-1 plasma levels by LDL-levels (mmol/L) group (<100, 100-129, 130-159, 160-189, 190+)

```{r Hypercholesterolemia}
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(LDLGroup = factor(case_when(LDL_finalCU < 100 ~ "<100",
                                                     LDL_finalCU >= 100  & LDL_finalCU <= 129 ~ "100-129",
                                                     LDL_finalCU >= 130  & LDL_finalCU <= 159 ~ "130-159",
                                                     LDL_finalCU >= 160  & LDL_finalCU <= 189 ~ "160-189",
                                                     LDL_finalCU >= 190 ~ "190+"))) 


table(AEDB.CEA$LDLGroup, AEDB.CEA$Gender)


```

Now we can draw some graphs of plasma/plaque MCP1 levels per sex and hypercholesterolemia/LDL-levels group, as well as stratified by lipid-lowering drugs users.

### Inverse-rank transformed data

#### MCP1 plaque levels

```{r MCP1 per Hypercholesterolemia per Sex, ranked}

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), 
                  x = c("LDLGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "LDL (mg/dL) per LLD use",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")

```

#### MCP1 plasma levels

> NOT AVAILABLE YET

```{r MCP1 per Hypercholesterolemia per Sex, ranked plasma}
# compare_means(MCP1_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("Gender"),
                  y = "MCP1_rank",
                  xlab = "Gender",
                  ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("AgeGroup"),
                  y = "MCP1_rank",
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")

```

### Raw data

Simalarly but now for the raw data as median ± interquartile range.

#### MCP1 plaque levels


```{r MCP1 per Hypercholesterolemia per Sex}

# ?ggpubr::ggboxplot()
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))

ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), 
                  x = c("LDLGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "LDL (mg/dL) per LLD use",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Med.Statin.LLD",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
```

### MCP1 plasma levels

> NOT AVAILABLE YET

```{r MCP1 per Hypercholesterolemia per Sex, plasma}
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1", 
                  xlab = "Gender",
                  ylab = "MCP1 plasma [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plasma [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
```


## Kidney function (eGFR)
We want to create figures of MCP1 levels stratified by kidney function. 

- Box and Whisker plot for MCP-1 plaque levels by chronic kidney disease (CKD) group (1, 2, 3, 4, 5)
- Box and Whisker plot for MCP-1 plasma levels by eGFR (MDRD-based) group (90+, 60-89, 30-59, <30)

```{r EGFR}
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(eGFRGroup = factor(case_when(GFR_MDRD < 15 ~ "<15",
                                                             GFR_MDRD >= 15  & GFR_MDRD <= 29 ~ "15-29",
                                                             GFR_MDRD >= 30  & GFR_MDRD <= 59 ~ "30-59",
                                                             GFR_MDRD >= 60  & GFR_MDRD <= 89 ~ "60-89",
                                                             GFR_MDRD >= 90 ~ "90+")))

table(AEDB.CEA$eGFRGroup, AEDB.CEA$Gender)

table(AEDB.CEA$eGFRGroup, AEDB.CEA$KDOQI)

```

Now we can draw some graphs of plasma/plaque MCP1 levels per sex and kidney function group.

### Inverse-rank transformed data

#### MCP1 plaque levels

```{r MCP1 per EGFR per Sex, ranked}

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "eGFR (mL/min per 1.73 m2) per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Kidney function (KDOQI) per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), 
                  x = c("eGFRGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)

```

#### MCP1 plasma levels

> NOT AVAILABLE YET

```{r MCP1 per EGFR per Sex, ranked plasma}
# compare_means(MCP1_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("Gender"),
                  y = "MCP1_rank",
                  xlab = "Gender",
                  ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("AgeGroup"),
                  y = "MCP1_rank",
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")

```

### Raw data

Simalarly but now for the raw data as median ± interquartile range.

#### MCP1 plaque levels


```{r MCP1 per EGFR per Sex}

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "eGFR (mL/min per 1.73 m2) per gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Kidney function (KDOQI) per gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), 
                  x = c("eGFRGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)

```

### MCP1 plasma levels

> NOT AVAILABLE YET

```{r MCP1 per EGFR per Sex, plasma}
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1", 
                  xlab = "Gender",
                  ylab = "MCP1 plasma [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plasma [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
```


## BMI
We want to create figures of MCP1 levels stratified by BMI. 

- Box and Whisker plot for MCP-1 plaque levels by BMI WHO group (underweight, normal, overweight, obese)
- Box and Whisker plot for MCP-1 plasma levels by BMI group (<18.5, 18.5-24.9, 25, 29.9, 30-24.9, 35+)

```{r BMI}
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(BMIGroup = factor(case_when(BMI < 18.5 ~ "<18.5",
                                                     BMI >= 18.5  & BMI < 25 ~ "18.5-24",
                                                     BMI >= 25  & BMI < 30 ~ "25-29",
                                                     BMI >= 30  & BMI < 35 ~ "30-35",
                                                     BMI >= 35 ~ "35+"))) 

# require(labelled)
# AEDB.CEA$BMI_US <- as_factor(AEDB.CEA$BMI_US)
# AEDB.CEA$BMI_WHO <- as_factor(AEDB.CEA$BMI_WHO)
# table(AEDB.CEA$BMI_WHO, AEDB.CEA$BMI_US)

table(AEDB.CEA$BMIGroup, AEDB.CEA$Gender)
table(AEDB.CEA$BMIGroup, AEDB.CEA$BMI_WHO)

```

Now we can draw some graphs of plasma/plaque MCP1 levels per sex and age group.

### Inverse-rank transformed data

#### MCP1 plaque levels

```{r MCP1 per BMI per Sex, ranked}

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "BMI groups (kg/m2)",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "BMI groups (kg/m2)",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "BMI groups (kg/m2) per WHO categories",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "BMI_WHO",
                  palette = "npg",
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)

```

#### MCP1 plasma levels

> NOT AVAILABLE YET

```{r MCP1 per BMI per Sex, ranked plasma}
# compare_means(MCP1_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("Gender"),
                  y = "MCP1_rank",
                  xlab = "Gender",
                  ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("AgeGroup"),
                  y = "MCP1_rank",
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")

```

### Raw data

Simalarly but now for the raw data as median ± interquartile range.

#### MCP1 plaque levels


```{r MCP1 per BMI per Sex}

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "BMI groups (kg/m2)",
                  ylab = "MCP1 plaque [pg/ug]",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "BMI groups (kg/m2)",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "BMI groups (kg/m2) per WHO categories",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "BMI_WHO",
                  palette = "npg",
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
```

### MCP1 plasma levels

> NOT AVAILABLE YET

```{r MCP1 per BMI per Sex, plasma}
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1", 
                  xlab = "Gender",
                  ylab = "MCP1 plasma [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plasma [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
```


## Diabetes
We want to create figures of MCP1 levels stratified by type 2 diabetes. 

- Box and Whisker plot for MCP-1 plaque levels by type 2 diabetes group (no, yes)

Now we can draw some graphs of plasma/plaque MCP1 levels per sex and age group.

### Inverse-rank transformed data

#### MCP1 plaque levels

```{r MCP1 per Diabetes per Sex, ranked}

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)), 
                  x = c("DiabetesStatus"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Diabetes status",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)), 
                  x = c("DiabetesStatus"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Diabetes status per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
```

#### MCP1 plasma levels

> NOT AVAILABLE YET

```{r MCP1 per Diabetes per Sex, ranked plasma}
# compare_means(MCP1_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("Gender"),
                  y = "MCP1_rank",
                  xlab = "Gender",
                  ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("AgeGroup"),
                  y = "MCP1_rank",
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")

```

### Raw data

Simalarly but now for the raw data as median ± interquartile range.

#### MCP1 plaque levels


```{r MCP1 per Diabetes per Sex}

# Global test
# compare_means(MCP1_pg_ug_2015 ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)), 
                  x = c("DiabetesStatus"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Diabetes status",
                  ylab = "MCP1 plaque [pg/ug]",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9",
                  add = c("median_iqr", "jitter")) #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_pg_ug_2015 ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)), 
                  x = c("DiabetesStatus"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Diabetes status per gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) #+
  # stat_compare_means(method = "kruskal.test")


```

### MCP1 plasma levels

> NOT AVAILABLE YET

```{r MCP1 per Diabetes per Sex, plasma}
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1", 
                  xlab = "Gender",
                  ylab = "MCP1 plasma [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plasma [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
```


## Smoking
We want to create figures of MCP1 levels stratified by smoking. 

- Box and Whisker plot for MCP-1 plaque levels by smoking group (never, ex, current)

Now we can draw some graphs of plasma/plaque MCP1 levels per sex and age group.

### Inverse-rank transformed data

#### MCP1 plaque levels

```{r MCP1 per Smoking per Sex, ranked}

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Smoker status",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9", 
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Smoker status per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
```

#### MCP1 plasma levels

> NOT AVAILABLE YET

```{r MCP1 per Smoking per Sex, ranked plasma}
# compare_means(MCP1_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("Gender"),
                  y = "MCP1_rank",
                  xlab = "Gender",
                  ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("AgeGroup"),
                  y = "MCP1_rank",
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")

```

### Raw data

Simalarly but now for the raw data as median ± interquartile range.

#### MCP1 plaque levels


```{r MCP1 per Smoking per Sex}

# Global test
# compare_means(MCP1_pg_ug_2015 ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Smoker status",
                  ylab = "MCP1 plaque [pg/ug]",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9", 
                  add = c("median_iqr", "jitter")) #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_pg_ug_2015 ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Smoker status per gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) #+
  # stat_compare_means(method = "kruskal.test")

```

### MCP1 plasma levels

> NOT AVAILABLE YET

```{r MCP1 per Smoking per Sex, plasma}
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1", 
                  xlab = "Gender",
                  ylab = "MCP1 plasma [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plasma [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
```


## Stenosis
We want to create figures of MCP1 levels stratified by stenosis grade. 

- Box and Whisker plot for MCP-1 plaque levels by stenosis grade group (<70, 70-89, 90+)

```{r Stenosis}
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(StenoticGroup = factor(case_when(stenose == "0-49%" ~ "<70",
                                                     stenose == "0-49%" ~ "<70",
                                                     stenose == "50-70%" ~ "<70",
                                                     stenose == "70-90%" ~ "70-89",
                                                     stenose == "50-99%" ~ "90+",
                                                     stenose == "70-99%" ~ "90+",
                                                     stenose == "100% (Occlusion)" ~ "90+",
                                                     stenose == "90-99%" ~ "90+")))

table(AEDB.CEA$StenoticGroup, AEDB.CEA$Gender)
table(AEDB.CEA$stenose, AEDB.CEA$StenoticGroup)

```

Now we can draw some graphs of plasma/plaque MCP1 levels per sex and age group.

### Inverse-rank transformed data

#### MCP1 plaque levels

```{r MCP1 per Stenosis per Sex, ranked}

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Stenotic grade",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Stenotic grade per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")

```

#### MCP1 plasma levels

> NOT AVAILABLE YET

```{r MCP1 per Stenosis per Sex, ranked plasma}
# compare_means(MCP1_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("Gender"),
                  y = "MCP1_rank",
                  xlab = "Gender",
                  ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("AgeGroup"),
                  y = "MCP1_rank",
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")

```

### Raw data

Simalarly but now for the raw data as median ± interquartile range.

#### MCP1 plaque levels


```{r MCP1 per Stenosis per Sex}

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Stenotic grade",
                  ylab = "MCP1 plaque [pg/ug]",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Stenotic grade per gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")


```

### MCP1 plasma levels

> NOT AVAILABLE YET

```{r MCP1 per Stenosis per Sex, plasma}
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("Gender"),
                  y = "MCP1", 
                  xlab = "Gender",
                  ylab = "MCP1 plasma [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plasma [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
```



## Circulating vs. plaque MCP1 levels

We will also make a nice correlation plot between plasma and plaque MCP1 levels.

> NOT AVAILABLE YET

```{r MCP1 correlations, plasma vs. plaque}

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_pg_ug_2015",
                  y = "MCP1", 
                  xlab = "MCP1 plaque [pg/ug]",
                  ylab = "MCP1 plasma [pg/mL]",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(8,750))

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_pg_ug_2015_rank",
                  y = "MCP1_rank", 
                  xlab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  ylab = "MCP1 plasma [pg/mL]\n(inverse-rank transformation)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(2,3))

```

## Plaque vs. plaque MCP1 levels
We will also make a nice correlation plot between the two experiments of plaque MCP1 levels. 

```{r MCP1 correlations, plaque vs. plaque}
AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
summary(AEDB.CEA$MCP1)
summary(AEDB.CEA$MCP1_pg_ug_2015)

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1", 
                  y = "MCP1_pg_ug_2015",
                  xlab = "MCP1 plaque [pg/mL] (exp. no. 1)",
                  ylab = "MCP1 plaque [pg/ug] (exp. no. 2)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"))

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_rank", 
                  y = "MCP1_pg_ug_2015_rank",
                  xlab = "MCP1 plaque [pg/mL]\n(INRT,  exp. no. 1)",
                  ylab = "MCP1 plaque [pg/ug]\n(INRT,  exp. no. 2)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"))

```


## Symptoms
We want to create per-symptom figures. 


```{r SymptomGroups}
library(dplyr)

table(AEDB.CEA$AgeGroup, AEDB.CEA$AsymptSympt2G)
table(AEDB.CEA$Gender, AEDB.CEA$AsymptSympt2G)
table(AEDB.CEA$AsymptSympt2G)

```

Now we can draw some graphs of plasma/plaque MCP1 levels per symptom group.
```{r MCP1 per SymptomGroups}

# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))

p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "AsymptSympt2G", y = "MCP1_pg_ug_2015_rank",
                  title = "MCP1 plaque [pg/ug] levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "MCP1 plaque [pg/ug]\n inverse-rank transformation",
                  color = "AsymptSympt2G", 
                  palette = c(uithof_color[16], uithof_color[23]),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
```

```{r MCP1 per SymptomGroups, plasma}
p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "AsymptSympt2G", y = "MCP1_rank",
                  title = "MCP1 plasma [pg/mL] levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "MCP1 plasma [pg/mL]\n inverse-rank transformation",
                  color = "AsymptSympt2G", 
                  palette = c(uithof_color[16], uithof_color[23]),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
rm(p1)
```

## Forest plots

We would also like to visualize the multivariable analyses results.
```{r load model data}
library(ggplot2)
library(openxlsx)
model1_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"))
model2_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"))
model1_mcp1$model <- "univariate"
model2_mcp1$model <- "multivariate"

models_mcp1 <- rbind(model1_mcp1, model2_mcp1)
models_mcp1

```

```{r forestplot plaque}
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]),
                  low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]),
                  high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]))

fp <- ggplot(data = dat, aes(x = group, y = cen, ymin = low, ymax = high)) +
  geom_pointrange(linetype = 2, size = 1, colour = c("#1290D9", "#49A01D")) + 
  geom_hline(yintercept = 1, lty = 2) +  # add a dotted line at x=1 after flip
  coord_flip(ylim = c(0.8, 1.7)) +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  ggtitle("Plaque MCP-1 levels (1 SD increment)") +
  theme_minimal()  # use a white background
print(fp)

rm(fp)
```


```{r forestplot plasma}
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_rank"]),
                  low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_rank"]),
                  high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_rank"]))

fp <- ggplot(data=dat, aes(x=group, y=cen, ymin=low, ymax=high)) +
  geom_pointrange() + 
  geom_hline(yintercept=1, lty=2) +  # add a dotted line at x=1 after flip
  coord_flip() +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  theme(text = element_text(size=14)) +
  ggtitle("plasma MCP-1 levels (1 SD increment)") +
  theme_minimal()  # use a white background
print(fp)
rm(fp)
```

## MCP1 vs. cytokines plaque levels correlations

We will plot the correlations of other cytokine plaque levels to the MCP1 plaque levels. These include:

- IL2
- IL4
- IL5
- IL6
- IL8
- IL9
- IL10
- IL12
- IL13
- IL21
- INFG
- TNFA
- MIF
- MCP1
- MIP1a
- RANTES
- MIG
- IP10
- Eotaxin1
- TARC
- PARC
- MDC
- OPG
- sICAM1
- VEGFA
- TGFB

In addition we will look at three metalloproteinases which were measured using an activity assay. 

- MMP2
- MMP8
- MMP9

The proteins were measured using FACS and LUMINEX. Given the different platforms used (FACS vs. LUMINEX), we will inverse rank-normalize these variables as well to scale them to the same scale as the MCP1 plaque levels.


We will set the measurements that yielded '0' to NA, as it is unlikely that any protein ever has exactly 0 copies. The '0' yielded during the experiment are due to the limits of the detection.

### Prepare data
```{r MCP1 vs Cytokines INRT}
cytokines <- c("IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", "IL13", "IL21", 
               "INFG", "TNFA", "MIF", "MCP1", "MIP1a", "RANTES", "MIG", "IP10", "Eotaxin1", 
               "TARC", "PARC", "MDC", "OPG", "sICAM1", "VEGFA", "TGFB")
metalloproteinases <- c("MMP2", "MMP8", "MMP9")

# fix names
names(AEDB.CEA)[names(AEDB.CEA) == "VEFGA"] <- "VEGFA"


proteins_of_interest <- c(cytokines, metalloproteinases)

proteins_of_interest_rank = unlist(lapply(proteins_of_interest, paste0, "_rank"))


# make variables numerics()
AEDB.CEA <- AEDB.CEA %>%
  mutate_each(funs(as.numeric), proteins_of_interest)
  
for(PROTEIN in 1:length(proteins_of_interest)){

  # UCORBIOGSAqc$Z <- NULL
  var.temp.rank = proteins_of_interest_rank[PROTEIN]
  var.temp = proteins_of_interest[PROTEIN]
  
  cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.rank,".\n"))
  cat(paste0("* changing ", var.temp, " to numeric.\n"))

  # AEDB.CEA <-  AEDB.CEA %>% mutate(AEDB.CEA[,var.temp] == replace(AEDB.CEA[,var.temp], AEDB.CEA[,var.temp]==0, NA))

  AEDB.CEA[,var.temp][AEDB.CEA[,var.temp]==0.000000]=NA

  cat(paste0("* standardising ", var.temp, 
             " (mean: ",round(mean(!is.na(AEDB.CEA[,var.temp])), digits = 6),
             ", n = ",sum(!is.na(AEDB.CEA[,var.temp])),").\n"))
  
  AEDB.CEA <- AEDB.CEA %>%
      mutate_at(vars(var.temp), 
        # list(Z = ~ (AEDB.CEA[,var.temp] - mean(AEDB.CEA[,var.temp], na.rm = TRUE))/sd(AEDB.CEA[,var.temp], na.rm = TRUE))
        list(RANK = ~ qnorm((rank(AEDB.CEA[,var.temp], na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA[,var.temp]))))
      )
  # str(UCORBIOGSAqc$Z)
  cat(paste0("* renaming RANK to ", var.temp.rank,".\n"))
  AEDB.CEA[,var.temp.rank] <- NULL
  names(AEDB.CEA)[names(AEDB.CEA) == "RANK"] <- var.temp.rank
}

# rm(var.temp, var.temp.rank)

```

### Visualize transformations

We will just visualize these transformations.

```{r MCP1 vs Cytokines Histograms}
proteins_of_interest_rank_mcp1 <- c("MCP1_pg_ug_2015_rank", "MCP1_pg_ml_2015_rank", proteins_of_interest_rank)

proteins_of_interest_mcp1 <- c("MCP1_pg_ug_2015", "MCP1_pg_ml_2015", proteins_of_interest)

for(PROTEIN in proteins_of_interest_mcp1){
  cat(paste0("Plotting protein ", PROTEIN, ".\n"))
  
  p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN, " plaque levels"),
                    xlab = "",
                    ggtheme = theme_minimal())
  print(p1)
  
}


for(PROTEIN in proteins_of_interest_rank_mcp1){
  cat(paste0("Plotting protein ", PROTEIN, ".\n"))
  
  p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN, " plaque levels"),
                    xlab = "inverse-normal transformation",
                    ggtheme = theme_minimal())
  print(p1)
  
}
  
```

### Correlations

Here we calculate correlations between `MCP1_pg_ug_2015` and 28 other cytokines (including `MCP1` as measured in experiment 1. We use Spearman's test, thus, correlations a given in _rho_. Please note the indications of measurement methods:

- _L_: LUMINEX
- _E_: ELISA
- _a_: activity assay

```{r MCP1 vs Cytokines correlations}
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)

# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c(proteins_of_interest_rank_mcp1)
                                    )

# str(AEDB.CEA.temp)
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

rename_proteins_of_interest_mcp1 <- c("MCP1 (L, exp2, pg/ug)", "MCP1 (L, exp2, pg/mL)", 
                                    "IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", 
                                    "IL13 (L)", "IL21 (L)", 
                                    "INFG", "TNFA", "MIF (L)", 
                                    "MCP1 (L, exp1)", "MIP1a (L)", "RANTES (L)", "MIG (L)", "IP10 (L)", 
                                    "Eotaxin1 (L)", "TARC (L)", "PARC (L)", "MDC (L)", 
                                    "OPG (L)", "sICAM1 (L)", "VEGFA (E)", "TGFB (E)", "MMP2 (a)", "MMP8 (a)", "MMP9 (a)")
colnames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)
rownames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")

# ++++++++++++++++++++++++++++
# flattenCorrMatrix
# ++++++++++++++++++++++++++++
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    row = rownames(cormat)[row(cormat)[ut]],
    column = rownames(cormat)[col(cormat)[ut]],
    cor  =(cormat)[ut],
    p = pmat[ut]
    )
}

corr_biomarkers.rank.df <- flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank)


names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "row"] <- "Cytokine_X"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "column"] <- "CytokineY"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "cor"] <- "SpearmanRho"

DT::datatable(corr_biomarkers.rank.df)

fwrite(corr_biomarkers.rank.df, file = paste0(OUT_loc, "/",Today,".correlation_cytokines.txt"))

```

```{r MCP1 vs Cytokines heatmap}
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
p1 <- ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           tl.cex = 6,
           # xlab = c("MCP1"),
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))
p1
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.png"), plot = last_plot())
rm(p1)

```

While visually actractive we are not necessarily interested in the correlations between all the cytokines, rather of MCP1 with other cytokines only.

```{r MCP1 vs Cytokines barplot}
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "MCP1 (L, exp2, pg/ug)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/29)
p_threshold
p1 <- ggbarplot(temp, x = "CytokineY", y = "SpearmanRho",
          fill = "CytokineY",               # change fill color by cyl
          # color = "white",            # Set bar border colors to white
          palette = uithof_color,            # jco journal color palett. see ?ggpar
          xlab = "Cytokine",
          ylab = expression("Spearman's"~italic(rho)),
          sort.val = "desc",          # Sort the value in dscending order
          sort.by.groups = FALSE,     # Don't sort inside each group
          x.text.angle = 45, # Rotate vertically x axis texts
          cex = 0.8
          )
ggpar(p1, legend = "bottom", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 9),
        axis.text.y = element_text(size = 9)) 

rm(p1)

temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "MCP1 (L, exp2, pg/mL)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/29)
p_threshold
p1 <- ggbarplot(temp, x = "CytokineY", y = "SpearmanRho",
          fill = "CytokineY",               # change fill color by cyl
          # color = "white",            # Set bar border colors to white
          palette = uithof_color,            # jco journal color palett. see ?ggpar
          xlab = "Cytokine",
          ylab = expression("Spearman's"~italic(rho)),
          sort.val = "desc",          # Sort the value in dscending order
          sort.by.groups = FALSE,     # Don't sort inside each group
          x.text.angle = 45, # Rotate vertically x axis texts
          cex = 0.8
          )
ggpar(p1, legend = "bottom", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 9),
        axis.text.y = element_text(size = 9)) 

rm(p1)

```

Another version - problably not good. 
```{r MCP1 vs Cytokines dotchart}

p1 <- ggdotchart(temp, x = "CytokineY", y = "p_log10",
           color = "CytokineY",                                # Color by groups
           palette = uithof_color, # Custom color palette
           xlab = "Cytokine",
           ylab = expression(log[10]~"("~italic(p)~")-value"),
           ylim = c(0, 6),
           sorting = "descending",                       # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           rotate = FALSE,                                # Rotate vertically
           # group = "CytokineY",                                # Order by groups
           dot.size = 8,                                 # Large dot size
           label = round(temp$SpearmanRho, digits = 3),                        # Add mpg values as dot labels
           font.label = list(color = "white", size = 8, 
                             vjust = 0.5)                   
           )
ggpar(p1, legend = "bottom", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 9),
        axis.text.y = element_text(size = 9))

rm(temp, p1)

```


## MCP1 vs. cytokines plaque levels `lm()`

### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines traits as a function of plasma/plaque MCP1 levels.
```{r CrossSec: Cytokines - linear regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
```

```{r CrossSec: Cytokines - linear regression MODEL1 RANK Writing}
DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.MCP1_Plaque.Cytokines_Plaques.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```



### Model 2

In this model we correct for _Age_, _Gender_, _year of surgery_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines as a function of plasma/plaque MCP1 levels.
```{r CrossSec: Cytokines - linear regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
```

```{r CrossSec: Cytokines - linear regression MODEL2 RANK, writing}
DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.MCP1_Plaque.Cytokines_Plaques.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

## MCP1 cytokines plaque levels vs. vulnerability index

Here we calculate the plaque instability/vulnerability index

```{r Plaque Vulnerability}
# Plaque vulnerability

table(AEDB.CEA$Macrophages.bin)
table(AEDB.CEA$Fat.bin_10)
table(AEDB.CEA$Collagen.bin)
table(AEDB.CEA$SMC.bin)
table(AEDB.CEA$IPH.bin)

# SPSS code

# 
# *** syntax- Plaque vulnerability**.
# COMPUTE Macro_instab = -999.
# IF macrophages.bin=2 Macro_instab=1.
# IF macrophages.bin=1 Macro_instab=0.
# EXECUTE.
# 
# COMPUTE Fat10_instab = -999.
# IF Fat.bin_10=2 Fat10_instab=1.
# IF Fat.bin_10=1 Fat10_instab=0.
# EXECUTE.
# 
# COMPUTE coll_instab=-999.
# IF Collagen.bin=2 coll_instab=0.
# IF Collagen.bin=1 coll_instab=1.
# EXECUTE.
# 
# 
# COMPUTE SMC_instab=-999.
# IF SMC.bin=2 SMC_instab=0.
# IF SMC.bin=1 SMC_instab=1.
# EXECUTE.
# 
# COMPUTE IPH_instab=-999.
# IF IPH.bin=0 IPH_instab=0.
# IF IPH.bin=1 IPH_instab=1.
# EXECUTE.
# 
# COMPUTE Instability=Macro_instab + Fat10_instab +  coll_instab + SMC_instab + IPH_instab.
# EXECUTE.

# Fix plaquephenotypes
attach(AEDB.CEA)
# mac instability
AEDB.CEA[,"MAC_Instability"] <- NA
AEDB.CEA$MAC_Instability[Macrophages.bin == -999] <- NA
AEDB.CEA$MAC_Instability[Macrophages.bin == "no/minor"] <- 0
AEDB.CEA$MAC_Instability[Macrophages.bin == "moderate/heavy"] <- 1

# fat instability
AEDB.CEA[,"FAT10_Instability"] <- NA
AEDB.CEA$FAT10_Instability[Fat.bin_10 == -999] <- NA
AEDB.CEA$FAT10_Instability[Fat.bin_10 == " <10%"] <- 0
AEDB.CEA$FAT10_Instability[Fat.bin_10 == " >10%"] <- 1

# col instability 
AEDB.CEA[,"COL_Instability"] <- NA
AEDB.CEA$COL_Instability[Collagen.bin == -999] <- NA
AEDB.CEA$COL_Instability[Collagen.bin == "no/minor"] <- 1
AEDB.CEA$COL_Instability[Collagen.bin == "moderate/heavy"] <- 0

# smc instability
AEDB.CEA[,"SMC_Instability"] <- NA
AEDB.CEA$SMC_Instability[SMC.bin == -999] <- NA
AEDB.CEA$SMC_Instability[SMC.bin == "no/minor"] <- 1
AEDB.CEA$SMC_Instability[SMC.bin == "moderate/heavy"] <- 0

# iph instability
AEDB.CEA[,"IPH_Instability"] <- NA
AEDB.CEA$IPH_Instability[IPH.bin == -999] <- NA
AEDB.CEA$IPH_Instability[IPH.bin == "no"] <- 0
AEDB.CEA$IPH_Instability[IPH.bin == "yes"] <- 1

detach(AEDB.CEA)

table(AEDB.CEA$MAC_Instability, useNA = "ifany")
table(AEDB.CEA$FAT10_Instability, useNA = "ifany")
table(AEDB.CEA$COL_Instability, useNA = "ifany")
table(AEDB.CEA$SMC_Instability, useNA = "ifany")
table(AEDB.CEA$IPH_Instability, useNA = "ifany")

# creating vulnerability index
AEDB.CEA <- AEDB.CEA %>% mutate(Plaque_Vulnerability_Index = factor(rowSums(.[grep("_Instability", names(.))], na.rm = TRUE)),
                                )

table(AEDB.CEA$Plaque_Vulnerability_Index, useNA = "ifany")

# str(AEDB.CEA$Plaque_Vulnerability_Index)

```

Here we plot the levels of inverse-rank normal transformed `MCP1` plaque levels from experiment 1 and 2 to the `Plaque vulnerability index`. 
```{r Fix ORyearGroup}
library(sjlabelled)
attach(AEDB.CEA)
AEDB.CEA$yeartemp <- as.numeric(year(AEDB.CEA$dateok))
AEDB.CEA[,"ORyearGroup"] <- NA
AEDB.CEA$ORyearGroup[yeartemp <= 2007] <- "< 2007"
AEDB.CEA$ORyearGroup[yeartemp > 2007] <- "> 2007"
detach(AEDB.CEA)

table(AEDB.CEA$ORyearGroup, AEDB.CEA$ORdate_year)
```

```{r MCP1 per PlaqueVulnerabilityIndex}
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Plaque_Vulnerability_Index",
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Plaque vulnerability index",
                  ylab = "MCP1 plaque [pg/ug]\n(INT, exp 2)",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Plaque_Vulnerability_Index",
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Plaque vulnerability index",
                  ylab = "MCP1 plaque [pg/mL]\n(INT, exp 2)",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Plaque vulnerability index")

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p3 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Plaque_Vulnerability_Index",
                  y = "MCP1_rank", 
                  xlab = "Plaque vulnerability index",
                  ylab = "MCP1 plaque [pg/mL]\n(INT, exp 1)",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p3, legend = "bottom", legend.title = "Plaque vulnerability index")


p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Plaque_Vulnerability_Index",
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Plaque vulnerability index",
                  ylab = "MCP1 plaque [pg/ug]\n(INT, exp 2)",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Plaque_Vulnerability_Index",
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Plaque vulnerability index",
                  ylab = "MCP1 plaque [pg/mL]\n(INT, exp 2)",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Plaque vulnerability index")

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p3 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Plaque_Vulnerability_Index",
                  y = "MCP1_rank", 
                  xlab = "Plaque vulnerability index",
                  ylab = "MCP1 plaque [pg/mL]\n(INT, exp 1)",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p3, legend = "bottom", legend.title = "Plaque vulnerability index")
```


### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of the plaque vulnerability indez as a function of plasma/plaque MCP1 levels.
```{r CrossSec: Plaque_Vulnerability_Index - ordinal regression MODEL1 RANK, include=TRUE, paged.print=TRUE}
TRAITS.PROTEIN.RANK.extra = c("MCP1_pg_ug_2015_rank", "MCP1_pg_ml_2015_rank",  "MCP1_rank")

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK.extra)) {
  PROTEIN = TRAITS.PROTEIN.RANK.extra[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "Plaque_Vulnerability_Index"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1, ORdate_epoch) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    # table(currentDF$ORdate_year)
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year, 
              data  =  currentDF, 
              Hess = TRUE)
    print(summary(fit))
    
    ## store table
    (ctable <- coef(summary(fit)))

    ## calculate and store p values
    p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
    
    ## combined table
    print((ctable <- cbind(ctable, "p value" = p)))
  }

```

### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis._.


```{r CrossSec: Plaque_Vulnerability_Index - ordinal regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

for (protein in 1:length(TRAITS.PROTEIN.RANK.extra)) {
  PROTEIN = TRAITS.PROTEIN.RANK.extra[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "Plaque_Vulnerability_Index"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, 
              data  =  currentDF, 
              Hess = TRUE)

    print(summary(fit))
    
    ## store table
    (ctable <- coef(summary(fit)))

    ## calculate and store p values
    p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
    
    ## combined table
    print((ctable <- cbind(ctable, "p value" = p)))
  }


```

## Comparison sample types OLINK-platform

We performed a pilot experiment comparing plasma and plaque-derived protein levels as measured using the OLINK platform.

My colleague, Arjan Boltjes, analyzed this. Below some graphs and some statistics. 

_estimate_:    -0.0004093809
_statistic_:   -0.003774306
_p.value_:     0.9969974
_parameter_:   85
_conf.low_:    -0.2110394
_conf.high_:   0.210257	
_method_:      Pearson's product-moment correlation	
_alternative:_ two.sided

**Figure 1: Distributions of plaque and plasma MCP1 levels.** Measured using the OLINK-platform (CVD-III panel). Pilot experiment with n = 88 samples. 
![Distributions plaque and plasma MCP1 levels](plasma_vs_bulk_OLINK_MCP1/figures/hist_olink-2.png)

**Figure 2: Comparison of plaque and plasma MCP1 levels.** Measured using the OLINK-platform (CVD-III panel). Pilot experiment with n = 88 samples. _AU_ = Arbitrary unit.
![Comparing plaque and plasma MCP1 levels (AU)](plasma_vs_bulk_OLINK_MCP1/figures/corr_olink-1.png)


# Session information

------

    Version:      v1.0.12
    Last update:  2020-07-06
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to analyse MCP1 from the Ather-Express Biobank Study.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
    
    **MoSCoW To-Do List**
    The things we Must, Should, Could, and Would have given the time we have.
    _M_
    * ASAP - analysis on plasma based on OLINK platform
    * DONE - analysis on the pilot dataset on the OLINK platform, comparing plasma vs. plaque
    * DONE - linear regression models (model 1 and model 2) of `MCP1_pg_ug_2015` with cytokines
    * DONE - check out the difference between the measuremens of `MCP1` and `MCP1_pg_ug_2015` > `MCP1_pg_ug_2015` and `MCP1_pg_ml_2015` give similar results, `MCP1_pg_ug_2015` is more correct as this is corrected for the total amount of protein in the protein-sample used for the measurement. 
    * DONE - double check the plotting of the MACE
    * DONE - add the statistics for the correlation of `MCP1_pg_ug_2015` with the cytokines
    * DONE - add the comparison between `MCP1`, `MCP1_pg_ml_2015`, and `MCP1_pg_ug_2015`
    * DONE - analysis in the context of year of surgery given Van Lammeren _et al._ 
    * DONE - add analysis on vulnerability index
    * DONE - add analysis on binary and ordinal plaque phenotypes
    * DONE - add boxplots of MCP1 levels stratified by confounders/variables
    
    _S_
    * DONE prettify forest plot
    
    _C_
    
    
    _W_
    
    
    **Changes log**
    * v1.0.12 Add boxplots of MCP1 levels stratified by confounder/variables.
    * v1.0.11 Add analysis of pilot data comparing OLINK-platform based MCP1 levels in plasma and plaque.
    * v1.0.10 Add analyses for all three `MCP1`, `MCP1_pg_ml_2015`, and `MCP1_pg_ug_2015`. Add comparison between `MCP1`, `MCP1_pg_ml_2015`, and `MCP1_pg_ug_2015`. Add (and fixed) ordinal regression. Double checked which measurement to use. 
    * v1.0.9 Added linear regression models for MCP1 vs. cytokines plaque levels. Double checked upload of MACE-plots. Added statistics from correlation (heatmap) to txt-file.
    * v1.0.8 Fixed error in MCP1 plasma analysis. It turns out the `MCP1` and `MCP1_pg_ug_2015` variables are _both_ measured in plaque, in two separate experiments, exp. no. 1 and exp. no. 2, respectively. 
    * v1.0.7 Fixed the per Age-group MCP1 Box plots. Added correlations with other cytokines in plaques.
    * v1.0.6 Only analyses and figures that end up in the final manuscript.
    * v1.0.5 Update with 30- and 90-days survival.
    * v1.0.4 Updated with Cox-regressions.
    * v1.0.3 Included more models.
    * v1.0.2 Bugs fixed.
    * v1.0.1 Extended with linear and logistic regressions.
    * v1.0.0 Inital version.
    

------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment
```{r Saving}
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".sample_selection.RData"))
```

------
<sup>&copy; 1979-2020 Sander W. van der Laan | s.w.vanderlaan-2[at]umcutrecht.nl | [swvanderlaan.github.io](https://swvanderlaan.github.io).</sup>
------

